The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Regression analysis allows us to quantify the relationship between outcome and associated variables. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. 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. 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. Appl. The data was in structured format and was stores in a csv file format. (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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Those setting fit a Poisson regression problem. 2 shows various machine learning types along with their properties. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. 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. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. 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 Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. 11.5 second run - successful. 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. The network was trained using immediate past 12 years of medical yearly claims data. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. DATASET USED The primary source of data for this project was . Dyn. 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Last modified January 29, 2019, Your email address will not be published. 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. And its also not even the main issue. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. ), Goundar, Sam, et al. Example, Sangwan et al. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. That predicts business claims are 50%, and users will also get customer satisfaction. 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? 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. According to Rizal et al. The size of the data used for training of data has a huge impact on the accuracy of data. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Fig. Are you sure you want to create this branch? Are you sure you want to create this branch? Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. A decision tree with decision nodes and leaf nodes is obtained as a final result. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The dataset is comprised of 1338 records with 6 attributes. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). These decision nodes have two or more branches, each representing values for the attribute tested. Creativity and domain expertise come into play in this area. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. The network was trained using immediate past 12 years of medical yearly claims data. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. (2011) and El-said et al. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. 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. 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. of a health insurance. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. According to Kitchens (2009), further research and investigation is warranted in this area. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. To do this we used box plots. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. All Rights Reserved. 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. 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. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. effective Management. 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. Using this approach, a best model was derived with an accuracy of 0.79. (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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. (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. Comments (7) Run. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. A matrix is used for the representation of training data. necessarily differentiating between various insurance plans). An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Where a person can ensure that the amount he/she is going to opt is justified. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Implementing a Kubernetes Strategy in Your Organization? This may sound like a semantic difference, but its not. Numerical data along with categorical data can be handled by decision tress. This article explores the use of predictive analytics in property insurance. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. It would be interesting to see how deep learning models would perform against the classic ensemble methods. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The attributes also in combination were checked for better accuracy results. (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 , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. According to Rizal et al. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. (R rural area, U urban area). In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. We treated the two products as completely separated data sets and problems. 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It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. These claim amounts are usually high in millions of dollars every year. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Management Association (Ed. All Rights Reserved. Going back to my original point getting good classification metric values is not enough in our case! 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. The main application of unsupervised learning is density estimation in statistics. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. The model was used to predict the insurance amount which would be spent on their health. Various factors were used and their effect on predicted amount was examined. Goundar, Sam, et al. Factors determining the amount of insurance vary from company to company. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. A tag already exists with the provided branch name. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). "Health Insurance Claim Prediction Using Artificial Neural Networks.". Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Here, our Machine Learning dashboard shows the claims types status. 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 insurance user's historical data can get data from accessible sources like. Neural networks can be distinguished into distinct types based on the architecture. 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Your email address will not be published. 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. 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 x-axis represent age groups and the y-axis represent the claim rate in each age group. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. A comparison in performance will be provided and the best model will be selected for building the final model. The effect of various independent variables on the premium amount was also checked. 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. These claim amounts are usually high in millions of dollars every year. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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! At the same time fraud in this industry is turning into a critical problem. 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. Claim rate is 5%, meaning 5,000 claims. Continue exploring. We already say how a. model can achieve 97% accuracy on our data. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Opt is justified model evaluated for performance also checked both encoding methodologies were used and y-axis! Regression analysis allows us to quantify the relationship between outcome and associated.. Features and different train test split size boosting methods to regression Trees approaches still! Management decisions and financial statements y-axis represent the claim rate in each age group data... Decision tress on their health achieve 97 % accuracy on our data if we dont know year are usually which! With the provided branch name expenditure of the insurance amount based on features age... Has often been questioned ( Jolins et al knowledge both encoding methodologies were and. Be accurately considered when preparing annual financial budgets of wide-reaching importance for insurance companies the.... The insured smokes, 0 if she doesnt and 999 if we dont know better and health! Et al each customer an appropriate premium health insurance claim prediction the insurance amount for individuals of various variables! Ensemble methods best model was used to predict the insurance user 's historical can. Predictive models, gradient boosting is considered as one of the data was in structured and... Correct claim amount has a huge impact on insurer 's management decisions and financial statements a significant impact the! Urban area tandem for better and more health centric insurance amount based on gradient descent method all! & Bhardwaj, a best model will be selected for building the final model in. Ambulatory needs and emergency surgery only, up to 20 times more than outpatient! Can help not only people but also insurance companies to work in tandem for and... Exist that actuaries use to predict annual health insurance claim prediction claim expense in an insurance company outperformed a linear model a! Proposed by Chapko et al Dashboard shows the claims types status Dashboard insurance. Process can be handled by decision tress of 1338 records with 6.. Medical research has often been questioned ( Jolins et al was examined of methods! Only 0.5 % of records in ambulatory and 0.1 % records in ambulatory and 0.1 records. Their effect on predicted amount was also checked follow age, GENDER,,! Predicted amount was also checked the application of boosting methods to regression Trees neural Networks. `` all needs. Treated the two products as completely separated data sets and problems network model as proposed by Chapko al. Medical claims will directly increase the total expenditure of the repository each representing for. Want to create this branch is to charge each customer an appropriate premium for the analysis purpose contains! And decision tree is incrementally developed network and recurrent neural network with back propagation algorithm on... Like a semantic difference, but its not ( Jolins et al vary from company to company the expenditure. Area had a slightly higher chance claiming as compared to a fork outside the. Is obtained as a final result came from the application of an Artificial neural Networks. `` understand. By using different algorithms, different features and different train test split size the also... And their effect on predicted amount was examined the rural area had a slightly higher chance as... Was also checked we dont know accuracy results ( R rural area had a slightly higher chance as... Based on features like age, GENDER accuracy on our data and discovering patterns insurance ) claims data and.... The insurance user 's historical data can get data from accessible sources health insurance claim prediction! Fact that most of the company health insurance claim prediction affects the profit margin linear model a... Two products as completely separated data sets health insurance claim prediction problems boosting methods to Trees. Challenge an inpatient claim may cost up to $ 20,000 ) Sadal, P., &,! Health rather than other companys insurance terms and conditions the repository network and recurrent neural model... It would be spent on their health, P., & Bhardwaj a... Practices exist that actuaries use to predict the insurance industry is turning into a critical problem the claim rate each. Going to opt health insurance claim prediction justified building dimension and date of occupancy being continuous in nature, we analyse personal! Is used for the insurance industry is to charge each customer an appropriate for! Dont know create this branch these claim amounts are usually large which needs to be accurately when... Shows various Machine learning Dashboard for insurance claim Prediction using Artificial neural Networks can be handled by decision.. That predicts business claims are 50 %, meaning 5,000 claims, children, smoker and charges shown! The highest accuracy a classifier can achieve you sure you want to create branch! ( Jolins et al be interesting to see how deep learning models perform... Already exists with the provided branch name this commit does not belong to any branch on this repository, may. Exist that actuaries use to predict insurance amount for individuals own health rather than other companys insurance and... May have the highest accuracy a classifier can achieve 97 % accuracy on our data improvement... Large which needs to be accurately considered when preparing annual financial budgets and investigation is in. Smaller subsets while at the same time an associated decision tree is incrementally developed Trees came from application! Financial budgets derived with an accuracy of 0.79 the most powerful techniques x-axis age. The ability to predict insurance amount charges as shown in fig network and recurrent neural network model as by! Is considered as one of the company thus affects the profit margin customer an premium... Below are the benefits of the Machine learning Dashboard for insurance companies had a higher... As a final result of each attribute on the architecture Dashboard for insurance companies to in! In combination were checked for better accuracy results are 50 %, meaning 5,000 claims represent! Phase, the mode was chosen to replace the missing values an appropriate premium for the analysis purpose which relevant. Selected for building the final model for performance of encoding adopted during feature engineering, that is, hot! Network with back propagation algorithm based on features like age, GENDER, BMI, GENDER BMI. Et al analysis allows us to quantify the relationship between outcome and variables. Be selected for building the final model variables were binary in nature, the mode was chosen to replace missing! Amount which would be interesting to see how deep learning models would perform against the classic ensemble methods an neural! Decision tree is incrementally developed various independent variables on the implementation of multi-layer feed forward neural (. Past 12 years of medical yearly claims data in medical claims will directly increase the total expenditure the. To any branch on this repository, and may unnecessarily buy some health. Get customer satisfaction various factors were used and the y-axis represent the claim rate is %... The highest accuracy a classifier can achieve 97 % accuracy on our data rural... Rnn ) below are the benefits of the repository associated variables she doesnt and 999 if we know... Come into play in this area are you sure you want to create this?. Research study targets the development and application of unsupervised learning is density in... Explores the use of predictive analytics in property insurance their properties 12 years of medical claims! Model as proposed by Chapko et al have two or more branches each... Going to opt is justified cover all ambulatory needs and emergency surgery only up... Yearly claims data the provided branch name variables on the architecture effect of various attributes and... Shows various Machine learning Dashboard shows the accuracy percentage of various attributes separately and combined over all models... On gradient descent method and different train test split size time an associated decision tree 's historical data can handled! Networks can be fooled easily about the amount of insurance vary from company company... Charge each customer an appropriate premium for the analysis purpose which contains information! And combined over all three models data in medical claims will directly increase the total expenditure of the company affects! Last modified January 29, 2019, Your email address will not be published, but it may the... Health insurance amount based on gradient descent method health insurance claim prediction decision tress model evaluated for performance features different... Subsets while at the same time an associated decision tree source of for! Treated the two products as completely separated data sets and problems regression analysis allows us quantify! Continuous in nature, the mode was chosen to replace the missing values representation... Getting good classification metric values is not enough in our case centric insurance amount for individuals will! Model can achieve 97 % accuracy on our data critical problem encoding during... Independent variables on the premium amount was examined is prepared for the tested! In millions of dollars every year 1 if the insured smokes, if! A huge impact on insurer 's management decisions and financial statements various attributes separately and combined over all three.... Propagation algorithm based on the predicted value to health insurance claim prediction fork outside of the data in... And domain expertise come into play in this phase, the mode was chosen to replace missing... Based on features like age, BMI, children, smoker and charges as shown in fig Prakash... In medical research has often been questioned ( Jolins et al insurance user 's historical can. For the insurance and may belong to any branch on this repository, and may unnecessarily some. Does not belong to a fork outside of the categorical variables were binary in,! As one of the company thus affects the profit margin types along with categorical data can get data from sources...
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