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. insurance claim prediction machine learning. A tag already exists with the provided branch name. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Regression analysis allows us to quantify the relationship between outcome and associated variables. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Are you sure you want to create this branch? 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. Claim rate, however, is lower standing on just 3.04%. Take for example the, feature. According to Zhang et al. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. The model used the relation between the features and the label to predict the amount. Continue exploring. Later the accuracies of these models were compared. 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 Prediction Using Artificial Neural Networks.". We already say how a. model can achieve 97% accuracy on our data. This amount needs to be included in the yearly financial budgets. 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. Data. Keywords Regression, Premium, Machine Learning. 11.5 second run - successful. Settlement: Area where the building is located. 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. These decision nodes have two or more branches, each representing values for the attribute tested. It also shows the premium status and customer satisfaction every . This may sound like a semantic difference, but its not. 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How can enterprises effectively Adopt DevSecOps? Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Creativity and domain expertise come into play in this area. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 11.5s. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. (2011) and El-said et al. 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? According to Zhang et al. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. For some diseases, the inpatient claims are more than expected by the insurance company. Dataset is not suited for the regression to take place directly. J. Syst. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The attributes also in combination were checked for better accuracy results. 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. Refresh the page, check. Introduction to Digital Platform Strategy? In this case, we used several visualization methods to better understand our data set. From the box-plots we could tell that both variables had a skewed distribution. Description. Using this approach, a best model was derived with an accuracy of 0.79. The insurance user's historical data can get data from accessible sources like. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. arrow_right_alt. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Attributes which had no effect on the prediction were removed from the features. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. According to Rizal et al. The network was trained using immediate past 12 years of medical yearly claims data. The different products differ in their claim rates, their average claim amounts and their premiums. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. In I. By filtering and various machine learning models accuracy can be improved. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. We see that the accuracy of predicted amount was seen best. (2016), ANN has the proficiency to learn and generalize from their experience. Comments (7) Run. During the training phase, the primary concern is the model selection. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This article explores the use of predictive analytics in property insurance. 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. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. However, it is. 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. Goundar, Sam, et al. (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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. It would be interesting to test the two encoding methodologies with variables having more categories. One of the issues is the misuse of the medical insurance systems. In the past, research by Mahmoud et al. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. That predicts business claims are 50%, and users will also get customer satisfaction. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Health Insurance Claim Prediction Using Artificial Neural Networks. Adapt to new evolving tech stack solutions to ensure informed business decisions. As a result, the median was chosen to replace the missing values. To do this we used box plots. Leverage the True potential of AI-driven implementation to streamline the development of applications. Application and deployment of insurance risk models . The data was imported using pandas library. 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. for the project. Coders Packet . The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. ), Goundar, Sam, et al. 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. 99.5% in gradient boosting decision tree regression. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The authors Motlagh et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The Company offers a building insurance that protects against damages caused by fire or vandalism. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. According to Rizal et al. Interestingly, there was no difference in performance for both encoding methodologies. Abhigna et al. Also it can provide an idea about gaining extra benefits from the health insurance. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. Neural networks can be distinguished into distinct types based on the architecture. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." However, this could be attributed to the fact that most of the categorical variables were binary in nature. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. 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. The distribution of number of claims is: Both data sets have over 25 potential features. These claim amounts are usually high in millions of dollars every year. The real-world data is noisy, incomplete and inconsistent. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. (2011) and El-said et al. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. For predictive models, gradient boosting is considered as one of the most powerful techniques. Health Insurance Cost Predicition. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. 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. Data. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Decision on the numerical target is represented by leaf node. 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. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. Example, Sangwan et al. You signed in with another tab or window. The primary source of data for this project was from Kaggle user Dmarco. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 1. Going back to my original point getting good classification metric values is not enough in our case! Dong et al. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Logs. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 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. All Rights Reserved. Claim rate is 5%, meaning 5,000 claims. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. 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. A tag already exists with the provided branch name. Machine Learning approach is also used for predicting high-cost expenditures in health care. Then the predicted amount was compared with the actual data to test and verify the model. However, training has to be done first with the data associated. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. Notebook. 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. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Early health insurance amount prediction can help in better contemplation of the amount needed. A comparison in performance will be provided and the best model will be selected for building the final model. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. 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. Insurance Claims Risk Predictive Analytics and Software Tools. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Numerical data along with categorical data can be handled by decision tress. Neural networks can be distinguished into distinct types based on the architecture. Performance will be provided and the label to predict the amount, research by Mahmoud al! Predict the amount needed the two encoding methodologies with variables having more categories thus affects profit. 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