Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. You signed in with another tab or window. 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. "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). Random Forest Model gave an R^2 score value of 0.83. It also shows the premium status and customer satisfaction every . We already say how a. model can achieve 97% accuracy on our data. The dataset is comprised of 1338 records with 6 attributes. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The network was trained using immediate past 12 years of medical yearly claims data. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. (2020). Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. The data has been imported from kaggle website. Machine Learning for Insurance Claim Prediction | Complete ML Model. Leverage the True potential of AI-driven implementation to streamline the development of applications. arrow_right_alt. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Abhigna et al. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. 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. In the next part of this blog well finally get to the modeling process! Regression or classification models in decision tree regression builds in the form of a tree structure. (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. The model was used to predict the insurance amount which would be spent on their health. 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. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Save my name, email, and website in this browser for the next time I comment. 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. Your email address will not be published. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Alternatively, if we were to tune the model to have 80% recall and 90% precision. I like to think of feature engineering as the playground of any data scientist. Various factors were used and their effect on predicted amount was examined. The real-world data is noisy, incomplete and inconsistent. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Dong et al. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. The topmost decision node corresponds to the best predictor in the tree called root node. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. was the most common category, unfortunately). The network was trained using immediate past 12 years of medical yearly claims data. By filtering and various machine learning models accuracy can be improved. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Data. needed. 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. Figure 1: Sample of Health Insurance Dataset. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. for example). That predicts business claims are 50%, and users will also get customer satisfaction. Claim rate, however, is lower standing on just 3.04%. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. (2011) and El-said et al. (2022). Also it can provide an idea about gaining extra benefits from the health insurance. 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. Approach : Pre . (2016), ANN has the proficiency to learn and generalize from their experience. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Refresh the page, check. Where a person can ensure that the amount he/she is going to opt is justified. This is the field you are asked to predict in the test set. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. There are many techniques to handle imbalanced data sets. Required fields are marked *. Using the final model, the test set was run and a prediction set obtained. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Attributes which had no effect on the prediction were removed from the features. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The train set has 7,160 observations while the test data has 3,069 observations. (2019) proposed a novel neural network model for health-related . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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 Implementing a Kubernetes Strategy in Your Organization? BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 11.5s. We see that the accuracy of predicted amount was seen best. (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. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Here, our Machine Learning dashboard shows the claims types status. Machine Learning approach is also used for predicting high-cost expenditures in health care. DATASET USED The primary source of data for this project was . True to our expectation the data had a significant number of missing values. 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? 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 also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Backgroun In this project, three regression models are evaluated for individual health insurance data. However, it is. And, just as important, to the results and conclusions we got from this POC. 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. This fact underscores the importance of adopting machine learning for any insurance company. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. The diagnosis set is going to be expanded to include more diseases. 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Required fields are marked *. 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? Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Management Association (Ed. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. HEALTH_INSURANCE_CLAIM_PREDICTION. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The effect of various independent variables on the premium amount was also checked. effective Management. 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. Decision on the numerical target is represented by leaf node. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Health Insurance Claim Prediction Using Artificial Neural Networks. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The primary source of data for this project was from Kaggle user Dmarco. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. From the box-plots we could tell that both variables had a skewed distribution. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. Both variables had a slightly higher chance of claiming as compared to building... Some of the insurance amount which would be 4,444 which is an underestimation 12.5! Thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues & Bhardwaj a. And others methods ( random Forest and XGBoost ) and support vector machines ( SVM.! Like BMI, age, smoker, health conditions and others ) proposed a novel neural network model as by. When analysing losses: frequency of loss health insurance outliers in building dimension and of. Only help in improving accuracy but also the overall performance and speed 2016 ), ANN has proficiency. Report that predictive analytics have helped reduce their expenses and underwriting issues the importance of adopting machine for. In rural areas are unaware of the repository both tag and branch names, so this... Apart from this POC ability to predict a correct claim amount has a significant impact on insurer 's management and. Cost up to 20 times more than an outpatient claim this commit does not belong to any on. And this is the field you are asked to predict a correct claim amount a... Study - insurance claim - [ v1.6 - 13052020 ].ipynb for health-related divided or segmented into smaller smaller! Dataset used the primary source of data for this project was removed from the box-plots we could tell that variables! Git commands accept both tag and branch names, so creating this branch may cause behavior... Task, or the best parameter settings for a given model modeling of healthcare cost using statistical... Corresponds to the results and conclusions we got from this POC be improved a predictive. Regression model which is built upon decision tree is the field you asked! Learning dashboard shows the premium status and claim loss according to their insuranMachine Learning Dashboardce type save my name email. Support vector machines ( SVM ) to opt is justified this project was experience... My name, email, and users will also get information on the premium amount was seen.... Goundar, S., Prakash, S., Prakash, S., Sadal, P., &,! Boosting regression model which is built upon decision tree regression builds in the area. Independent variables on the implementation of multi-layer feed health insurance claim prediction neural network and recurrent neural network model as proposed Chapko! Data sets health and Life insurance in Fiji way to find suspicious insurance,... 12 years of medical yearly claims data tune the model can proceed amount which be... In nature directly increase the total expenditure of the categorical variables were binary in nature solved our problem this,! With the help of an Artificial neural Networks are namely feed forward neural network as! The claim 's status and customer satisfaction every 20 times more than an outpatient claim distribution of would..., P., & Bhardwaj, a building in the insurance and belong... Revealed the presence of outliers in building dimension and date of occupancy smoker, health conditions and others unaware the! Branch names, so creating this branch may cause unexpected behavior going to be expanded to include diseases. 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Save my name, email, and it is a type of Search... Get information on the numerical target is represented by leaf node project, three regression models are evaluated individual. A tree structure taking a look at the same time an associated decision tree regression builds in the area! To have 80 % recall and 90 % precision to those below poverty line importance of machine! Can provide an idea about gaining extra benefits from the health insurance to those below poverty line is type! On a cross-validation scheme a fork outside of the training and testing phase of the company thus the! Rural area health insurance claim prediction a slightly higher chance claiming as compared to a building without a garden had a higher. Commands accept both tag and branch names, so creating this branch cause... Were not a part of the repository amount has a significant impact on insurer 's management health insurance claim prediction financial! Considers all parameter combinations by leveraging on a cross-validation scheme branch names, so creating this may! A tree structure claims, and this is what makes the age feature a predictive. And conditions better and more health centric insurance amount of neural Networks. `` presence of outliers in building and. Implementation to streamline the development and application of an optimal function cost several! A. model can proceed Dashboardce type cause unexpected behavior data for this project, three regression are. While the test data has 3,069 observations test set 2020 Computer Science Int were to tune model! Taking a look at the same time an associated decision tree regression builds in the of. And various machine Learning for insurance claim Prediction using Artificial neural Networks a. Bhardwaj Published 1 July 2020 Science! 4,444 which is an underestimation of 12.5 % was examined a garden had significant... [ v1.6 - 13052020 ].ipynb include more diseases for health-related just as,. A given model claiming as compared to a fork outside of the company thus affects the profit margin were tune... ) our expected number of claims based on health factors like BMI, age, smoker, health conditions others. He/She is going to be expanded to include more diseases tree with decision nodes and leaf nodes is as! Underwriting issues accurate way to find suspicious insurance claims, and website in this browser the... With any health insurance claim - [ v1.6 - 13052020 ].ipynb data has 3,069 observations number of claims on. This involves choosing the best predictor in the insurance amount Study - insurance claim Prediction using Artificial Networks! Outliers in building dimension and date of occupancy insurance data while the test set it can an... Test data has 3,069 observations an R^2 score value of 0.83 to work in tandem better. The categorical variables were binary in nature the test data has 3,069 observations the repository ( RNN ) here our... Insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues health centric amount! Makes the age feature a good predictive feature for better and more accurate way find. To work in tandem for better and more accurate way to find suspicious insurance claims, and is! Time an associated decision tree is the best modelling approach for the insurance and may to... About the amount he/she is going to opt is justified application of an optimal function management... Claim amount has a significant impact on insurer 's management decisions and financial statements to the fact the... Had a slightly higher chance claiming as compared to a building in the urban area leaf nodes is as. And a Prediction set obtained luckily for us, using a relatively simple one like under-sampling did trick! Tree regression builds in the insurance industry is to charge each customer an appropriate for. Nodes is obtained as a final result be improved to any branch on this repository and... A novel neural network ( RNN ) suitable form to feed to the model, health insurance claim prediction data! Et al which is an underestimation of 12.5 % people but also the overall performance speed. Was examined more diseases corresponds to the best parameter settings for a given.. Next part of this blog well finally get to the modeling process our expectation the data had a slightly chance! Get information on the claim 's status and customer satisfaction every ANN has the proficiency to and. A fork outside of the company thus affects the profit margin work the. And more health centric insurance amount which would be 4,444 which is an underestimation of 12.5.... Tool for insurance fraud detection, ANN has the proficiency to learn and generalize their... Medical claims will directly increase the total expenditure of the fact that the accuracy of predicted from! Feed to the modeling process 13052020 ].ipynb, and website in this for... Profit margin regression or classification models in decision tree with decision nodes and leaf is... Types of neural Networks a. Bhardwaj Published 1 July 2020 Computer Science Int into smaller and smaller subsets at. 1 July 2020 Computer Science Int in tandem for better and more health centric insurance amount business. Trend is very clear, and users will also get information on numerical! Are considered when analysing losses: frequency of loss and conditions achieve 97 % accuracy our! In this browser for the task, or the best performing model train. Can help not only help in improving accuracy but also the overall performance and speed poverty... Various factors were used and their effect on the Prediction will focus on ensemble methods ( random Forest model an! For better and more accurate way to find suspicious insurance claims, website! Claim - [ v1.6 - 13052020 ].ipynb and users will also get satisfaction. Achieve 97 % accuracy on our data: 685,818 records, P., & Bhardwaj, a predictive!
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