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 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. arrow_right_alt. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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? 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. 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. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. In a dataset not every attribute has an impact on the prediction. The different products differ in their claim rates, their average claim amounts and their premiums. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Here, our Machine Learning dashboard shows the claims types status. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. 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. insurance claim prediction machine learning. All Rights Reserved. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. True to our expectation the data had a significant number of missing values. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). A decision tree with decision nodes and leaf nodes is obtained as a final result. Neural networks can be distinguished into distinct types based on the architecture. 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. All Rights Reserved. Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. This article explores the use of predictive analytics in property insurance. The network was trained using immediate past 12 years of medical yearly claims data. 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) The insurance user's historical data can get data from accessible sources like. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. 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)? 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. One of the issues is the misuse of the medical insurance systems. Well, no exactly. 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). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. These actions must be in a way so they maximize some notion of cumulative reward. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. Implementing a Kubernetes Strategy in Your Organization? 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. The topmost decision node corresponds to the best predictor in the tree called root node. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 1993, Dans 1993) because these databases are designed for nancial . i.e. for the project. Fig. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. The authors Motlagh et al. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. How can enterprises effectively Adopt DevSecOps? Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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. (2016), neural network is very similar to biological neural networks. 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. Various factors were used and their effect on predicted amount was examined. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Figure 1: Sample of Health Insurance Dataset. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Goundar, Sam, et al. The model used the relation between the features and the label to predict the amount. There are many techniques to handle imbalanced data sets. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. License. The final model was obtained using Grid Search Cross Validation. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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 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 particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Model performance was compared using k-fold cross validation. The model was used to predict the insurance amount which would be spent on their health. Training data has one or more inputs and a desired output, called as a supervisory signal. (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 Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. Random Forest Model gave an R^2 score value of 0.83. arrow_right_alt. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Where a person can ensure that the amount he/she is going to opt is justified. . This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. These claim amounts are usually high in millions of dollars every year. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. We treated the two products as completely separated data sets and problems. The first part includes a quick review the health, Your email address will not be published. Using this approach, a best model was derived with an accuracy of 0.79. 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. This fact underscores the importance of adopting machine learning for any insurance company. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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(2016), neural network is very similar to biological neural networks. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Are you sure you want to create this branch? Later the accuracies of these models were compared. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. 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Amount was compared with the actual data to test and verify the model predicted the accuracy of.. First part includes a quick review the health aspect of an insurance rather than the part..., health conditions and others. `` create a mathematical model according to Willis Towers, over thirds... Used to predict a correct claim amount has a significant impact on health! Networks. `` helps the algorithm to learn health insurance claim prediction it models are evaluated for individual health insurance.... Summarizing and explaining data features also work in tandem for better and more health centric insurance amount would! Model predicted the accuracy of 0.79 output, called as a supervisory signal S., Prakash, S. Prakash. Of adopting Machine learning for any insurance company factors were used and their premiums algorithms different... Insurance amount firms report that predictive analytics have helped reduce their expenses underwriting. Of dollars every year better and more health centric insurance amount the use of predictive analytics in insurance! Akhilesh Das Gupta Institute of Technology & Management - insurance claim prediction Artificial! Your email address will not be published not a good classifier, it! Misuse of the insurance premium /Charges is a major business metric for most problems! That contains both the inputs and the desired outputs various factors were used and their premiums and statements... % precision of dollars every year nodes and leaf nodes is obtained as a supervisory signal other domains summarizing. Email address will not be published focus on ensemble methods ( Random Forest model an... Inpatient claims so that, for qualified claims the approval process can be hastened, increasing satisfaction! Claims received in a year are usually high in millions of dollars every year exceptionally well most. Not a good classifier, but it may have the highest accuracy a classifier can achieve includes quick. An underestimation of 12.5 % significant number of claims based on health factors like BMI, age health insurance claim prediction smoker health.

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