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 simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. A tag already exists with the provided branch name. DATASET USED The primary source of data for this project was . 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Logs. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? 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. Users can quickly get the status of all the information about claims and satisfaction. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Accurate prediction gives a chance to reduce financial loss for the company. Health Insurance Cost Predicition. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Claim rate is 5%, meaning 5,000 claims. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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. 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. 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. 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. In I. 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 . Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. All Rights Reserved. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. That predicts business claims are 50%, and users will also get customer satisfaction. (2011) and El-said et al. For some diseases, the inpatient claims are more than expected by the insurance company. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Dr. Akhilesh Das Gupta Institute of Technology & Management. 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. To do this we used box plots. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. history Version 2 of 2. Currently utilizing existing or traditional methods of forecasting with variance. Keywords Regression, Premium, Machine Learning. Example, Sangwan et al. And, just as important, to the results and conclusions we got from this POC. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 11.5 second run - successful. (2016), neural network is very similar to biological neural networks. And its also not even the main issue. Various factors were used and their effect on predicted amount was examined. (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. Description. According to Zhang et al. Dataset was used for training the models and that training helped to come up with some predictions. Coders Packet . (2016), neural network is very similar to biological neural networks. Insurance companies are extremely interested in the prediction of the future. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Insurance Claims Risk Predictive Analytics and Software Tools. Creativity and domain expertise come into play in this area. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. The website provides with a variety of data and the data used for the project is an insurance amount data. 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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Also with the characteristics we have to identify if the person will make a health insurance claim. (2020). 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. Are you sure you want to create this branch? Comments (7) Run. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. Save my name, email, and website in this browser for the next time I comment. The data was imported using pandas library. 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. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Required fields are marked *. 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. These decision nodes have two or more branches, each representing values for the attribute tested. 99.5% in gradient boosting decision tree regression. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. This amount needs to be included in 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. (2016), ANN has the proficiency to learn and generalize from their experience. Abhigna et al. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. age : age of policyholder sex: gender of policy holder (female=0, male=1) The authors Motlagh et al. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. 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 data was in structured format and was stores in a csv file format. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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 Company offers a building insurance that protects against damages caused by fire or vandalism. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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. 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. Interestingly, there was no difference in performance for both encoding methodologies. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Then the predicted amount was compared with the actual data to test and verify the model. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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 key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Figure 1: Sample of Health Insurance Dataset. A tag already exists with the provided branch name. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. 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. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. It also shows the premium status and customer satisfaction every . 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. In this case, we used several visualization methods to better understand our data set. We treated the two products as completely separated data sets and problems. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. necessarily differentiating between various insurance plans). 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. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Backgroun In this project, three regression models are evaluated for individual health insurance data. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Numerical data along with categorical data can be handled by decision tress. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. . 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Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $ 330 billion Americans!