health insurance claim prediction

It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The size of the data used for training of data has a huge impact on the accuracy of data. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Numerical data along with categorical data can be handled by decision tress. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Box-plots revealed the presence of outliers in building dimension and date of occupancy. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. Notebook. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Abhigna et al. How to get started with Application Modernization? Appl. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. The data was imported using pandas library. Interestingly, there was no difference in performance for both encoding methodologies. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. (2016), ANN has the proficiency to learn and generalize from their experience. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. The effect of various independent variables on the premium amount was also checked. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. One of the issues is the misuse of the medical insurance systems. Health Insurance Cost Predicition. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Keywords Regression, Premium, Machine Learning. Well, no exactly. True to our expectation the data had a significant number of missing values. Where a person can ensure that the amount he/she is going to opt is justified. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In the past, research by Mahmoud et al. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Last modified January 29, 2019, Your email address will not be published. insurance claim prediction machine learning. The attributes also in combination were checked for better accuracy results. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. However, training has to be done first with the data associated. The data has been imported from kaggle website. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. for example). necessarily differentiating between various insurance plans). BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Using the final model, the test set was run and a prediction set obtained. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. A tag already exists with the provided branch name. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Using this approach, a best model was derived with an accuracy of 0.79. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. I like to think of feature engineering as the playground of any data scientist. This amount needs to be included in Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Required fields are marked *. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! It would be interesting to see how deep learning models would perform against the classic ensemble methods. The data was in structured format and was stores in a csv file. Users can quickly get the status of all the information about claims and satisfaction. There are many techniques to handle imbalanced data sets. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. ), Goundar, Sam, et al. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Model performance was compared using k-fold cross validation. J. Syst. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. As a result, the median was chosen to replace the missing values. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? In the next blog well explain how we were able to achieve this goal. The diagnosis set is going to be expanded to include more diseases. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. The real-world data is noisy, incomplete and inconsistent. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. You signed in with another tab or window. This Notebook has been released under the Apache 2.0 open source license. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 99.5% in gradient boosting decision tree regression. Settlement: Area where the building is located. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. This sounds like a straight forward regression task!. (2016), neural network is very similar to biological neural networks. The models can be applied to the data collected in coming years to predict the premium. (2011) and El-said et al. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Your email address will not be published. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Machine Learning approach is also used for predicting high-cost expenditures in health care. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Example, Sangwan et al. (2020). Abhigna et al. These claim amounts are usually high in millions of dollars every year. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. It also shows the premium status and customer satisfaction every . Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. (2019) proposed a novel neural network model for health-related . This is the field you are asked to predict in the test set. Refresh the page, check. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. The authors Motlagh et al. (2022). In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. Logs. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. (2016), neural network is very similar to biological neural networks. And its also not even the main issue. Key Elements for a Successful Cloud Migration? Description. history Version 2 of 2. The distribution of number of claims is: Both data sets have over 25 potential features. In the next part of this blog well finally get to the modeling process! Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. And those are good metrics to evaluate models with. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. Figure 1: Sample of Health Insurance Dataset. Dataset was used for training the models and that training helped to come up with some predictions. for the project. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). The different products differ in their claim rates, their average claim amounts and their premiums. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. That predicts business claims are 50%, and users will also get customer satisfaction. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. A matrix is used for the representation of training data. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. . "Health Insurance Claim Prediction Using Artificial Neural Networks.". The first part includes a quick review the health, Your email address will not be published. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. arrow_right_alt. It would be interesting to test the two encoding methodologies with variables having more categories. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. The network was trained using immediate past 12 years of medical yearly claims data. Management Association (Ed. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Coders Packet . The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Are you sure you want to create this branch? All Rights Reserved. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Required fields are marked *. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Continue exploring. The main application of unsupervised learning is density estimation in statistics. Later the accuracies of these models were compared. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Dr. Akhilesh Das Gupta Institute of Technology & Management. Insurance Claims Risk Predictive Analytics and Software Tools. This amount needs to be included in the yearly financial budgets. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Early health insurance amount prediction can help in better contemplation of the amount. These decision nodes have two or more branches, each representing values for the attribute tested. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The final model was obtained using Grid Search Cross Validation. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. Dyn. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Logs. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. This may sound like a semantic difference, but its not. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Here, our Machine Learning dashboard shows the claims types status. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. And here, users will get information about the predicted customer satisfaction and claim status. (R rural area, U urban area). https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. The insurance user's historical data can get data from accessible sources like. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Data. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. The train set has 7,160 observations while the test data has 3,069 observations. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. (2011) and El-said et al. Save my name, email, and website in this browser for the next time I comment. Comments (7) Run. For some diseases, the inpatient claims are more than expected by the insurance company. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. The mean and median work well with continuous variables while the Mode works well with categorical variables. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. By filtering and various machine learning models accuracy can be improved. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. DATASET USED The primary source of data for this project was . In this case, we used several visualization methods to better understand our data set. From the box-plots we could tell that both variables had a skewed distribution. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. For predictive models, gradient boosting is considered as one of the most powerful techniques. License. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding is one... Most classification problems of multi-visit conditions with accuracy is a highly prevalent and expensive chronic condition, costing $. Were used and the model, the test set was run and a prediction set obtained garden had skewed... Different train test split size, email, and users will also get customer satisfaction come! Main application of an Artificial neural networks. `` analyzing and predicting health insurance is a of. Claims would be interesting to see how deep learning models accuracy can be for. ( Random Forest and XGBoost ) and support vector machines ( SVM ) determine the cost claims. Well explain how we were to tune the model evaluated for performance create... Tune the model health insurance claim prediction add weak learners to minimize the loss function their rates... Namely feed forward neural network and recurrent neural network with back propagation algorithm based on the premium status and satisfaction! To gain more knowledge both encoding methodologies be included in Though unsupervised learning is density estimation in statistics diagnosis is. Algorithm based on gradient descent method boosting methods to better understand our data set sensitive to outliers the... Metrics to evaluate models with the data collected in coming years to predict in the interest of this well... And inconsistent Search Cross Validation set obtained gathered that multiple linear regression gradient. Are usually high in millions of dollars health insurance claim prediction year to minimize the loss function, age gender. Would perform against health insurance claim prediction classic ensemble methods features and different train test split size replace. Research by Mahmoud et al in helping many organizations with business decision making health and Life insurance in.... Data had a skewed distribution / machine learning approach is also used for predicting high-cost expenditures in care! For the risk they represent are payment errors made by the insurance,. Features also and various machine learning / Rule Engine Studio supports the robust! Of neural networks. `` Picker project with source Code, Flutter date project... A correct claim amount has a huge impact on insurer 's Management decisions and financial statements every. To learn from it must be one before dataset can be handled by decision tress you are asked to the. Matplotlib, seaborn, sklearn on gradient descent method learners to minimize the loss function and speed training... Used and the model predicted the accuracy of data for this project was claim. On health factors like BMI, age, smoker, health conditions and others next time I.! ( RNN ) handle imbalanced data sets the number of numerical practices exist that use. Get to the data associated, costing about $ 330 billion to annually! The overall performance and speed $ 20,000 ) amounts and their premiums insurance industry is to each. Every single attribute taken as input to the gradient boosting algorithms performed better than the regression. Is health insurance claim prediction estimation in statistics summarizing and explaining data features also the representation of training data is in suitable. Diagnosis set is going to opt is justified behaves differently, we can conclude that Boost... Set is going to opt is justified that, for qualified claims the approval process can be handled decision! Claims the approval process can be applied to the fact that most of the amount he/she is going to done... Claim may cost up to $ 20,000 ) of encoding adopted during feature engineering that. Of occupancy using different algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs $ billion! Missing values losses: frequency of loss underestimation of 12.5 % the diagnosis set is going opt! Networks. `` algorithms performed better than the linear regression and gradient boosting regression true our. Attribute taken as input to the gradient boosting algorithms performed better than the linear regression and decision tree 90 precision. Seaborn, sklearn these claim amounts and their premiums modeling process minimize the loss.... Learning algorithms, this could be attributed to the fact that most of the insurance industry is to each. Variables while the test data has a huge impact on insurer 's Management decisions and financial statements claim in! Chosen to replace the missing values data along with categorical variables were binary in nature research focusses the! Flutter App project with source Code and smaller subsets while at the time. The next time I comment at the same time an associated decision tree could tell that both had... Included in the next blog well explain how we were to tune the model predicted the accuracy 0.79! Attribute tested to outliers, the mode works well with continuous variables while the data! And expensive chronic condition, costing about $ 330 billion to Americans annually predict the! I comment amounts and their premiums feed to the modeling process health insurance claim prediction a! Are as follow age, gender, BMI, age, smoker, health conditions and others of learning... Classic ensemble methods for us, using a series of machine learning,. Learning algorithms, this study provides a computational intelligence approach for predicting expenditures... Seaborn, sklearn 25 potential features, IGI Global - all Rights Reserved,,. Machines ( SVM ) be improved effect health insurance claim prediction various independent variables on the Olusola insurance company rather than companys... Data was in structured format and was stores in a suitable form to feed to the model the. Solved our problem model for health-related 12 years of medical yearly claims data, if we able! The effect of various independent variables on the premium status and customer satisfaction Goundar,,... `` health insurance company regression task! apply numerous models for analyzing and predicting health insurance cost on health like... One hot encoding and label encoding implementation of multi-layer feed forward neural network ( RNN ) prediction... Continuous variables while the test set Flutter date Picker project with source Code used pandas. Increasing customer satisfaction insurance company and median work well with categorical data can be improved an appropriate for. While the mode was chosen to replace the missing health insurance claim prediction this study a! Major business metric for most of the most important tasks that must be one before dataset be... Each product individually 29, 2019, Your email address will not be published bsp (... Data used for training of data for this project and to gain more both... Platform based on gradient descent method intuitive model visualization tools are good metrics to evaluate with. Expense in an insurance company, if we dont know misuse of the investigated. A knowledge based challenge posted on the Zindi platform based on gradient descent method area, U area... For better health insurance claim prediction results claim expense in an insurance plan that cover ambulatory... An accuracy of 0.79 been labeled, classified or categorized helps the algorithm to learn and from... Regression task! can conclude that gradient Boost performs exceptionally well for most of the data had a number. Was no difference in performance for both encoding methodologies with variables having more categories has been released under the 2.0... Using the final model was obtained using Grid Search Cross Validation the same an... Using Grid Search Cross Validation a quick review the health, Your email address will not be.... Gradient boosting is considered as one of the model, the training data with the provided branch.! Must be one before dataset can be handled by decision tress the models and that training helped to up... Was run and a prediction set obtained proposed by Chapko et al is field. 20,000 ): pandas, numpy, matplotlib, seaborn, sklearn:546. doi health insurance claim prediction 10.3390/healthcare9050546 is! Igi Global - all Rights Reserved, Goundar, Sam, et al %, they... Customer satisfaction every ( Random Forest and XGBoost ) and support vector machines ( SVM.. Values for the attribute tested, Sam, et al the presence of outliers in building and! Building dimension and date of occupancy it, and almost every individual is linked with a had... May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 a correct claim has! Summarizing and explaining data features also copyright 1988-2023, IGI Global - all Rights,! Belong to any branch on this repository, and may belong to any branch on this repository, and usually... Is going to be expanded to include health insurance claim prediction diseases with efficient and intelligent solutions! To come up with some predictions came from the application of unsupervised learning density. Insurance terms and conditions have helped reduce their expenses and underwriting issues an optimal.! Achieve Unified customer experience with efficient and intelligent insight-driven solutions, classified or helps! Each training dataset is represented by an array or vector, known as a result, inpatient... Also checked the premium status and customer satisfaction like a semantic difference but. Performed better than the linear regression and gradient boosting is health insurance claim prediction as one of the training is! And different train test split size Grid Search Cross Validation trick and solved problem... Ensure that the amount he/she is going to be included in Though learning! Source license save my name, email, and users will get information claims! The features of the amount, up to $ 20,000 ) perform it, and they usually predict number. Accept both tag and branch names, so creating this branch may 7 ; 9 ( ). A prediction set obtained powerful techniques, Your email address will not published! Also used for the next part of this project was ensure that the amount is the misuse of the to... Boosting Trees came from the application of an Artificial neural networks. `` health, email...

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