Fig. REFERENCES Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Claim rate, however, is lower standing on just 3.04%. of a health insurance. In the next part of this blog well finally get to the modeling process! The model was used to predict the insurance amount which would be spent on their health. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. Well, no exactly. The dataset is comprised of 1338 records with 6 attributes. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. In the past, research by Mahmoud et al. 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. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. HEALTH_INSURANCE_CLAIM_PREDICTION. 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) insurance claim prediction machine learning. 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. Required fields are marked *. Regression analysis allows us to quantify the relationship between outcome and associated variables. During the training phase, the primary concern is the model selection. That predicts business claims are 50%, and users will also get customer satisfaction. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. How can enterprises effectively Adopt DevSecOps? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The model used the relation between the features and the label to predict the amount. And those are good metrics to evaluate models with. License. Continue exploring. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. The network was trained using immediate past 12 years of medical yearly claims data. 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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 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. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. 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. Goundar, Sam, et al. All Rights Reserved. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. For some diseases, the inpatient claims are more than expected by the insurance company. That predicts business claims are 50%, and users will also get customer satisfaction. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Creativity and domain expertise come into play in this area. The network was trained using immediate past 12 years of medical yearly claims data. Health Insurance Claim Prediction Using Artificial Neural Networks. ). One of the issues is the misuse of the medical insurance systems. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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 insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Also it can provide an idea about gaining extra benefits from the health insurance. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. 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? A matrix is used for the representation of training data. (2020). Abhigna et al. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Decision on the numerical target is represented by leaf node. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. trend was observed for the surgery data). "Health Insurance Claim Prediction Using Artificial Neural Networks.". needed. The x-axis represent age groups and the y-axis represent the claim rate in each age group. The real-world data is noisy, incomplete and inconsistent. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. (2019) proposed a novel neural network model for health-related . 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. Description. 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)? 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. Take for example the, feature. The data has been imported from kaggle website. The final model was obtained using Grid Search Cross Validation. Random Forest Model gave an R^2 score value of 0.83. The train set has 7,160 observations while the test data has 3,069 observations. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. 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. 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. was the most common category, unfortunately). Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. effective Management. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. (2022). In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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%. (2016), neural network is very similar to biological neural networks. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. (R rural area, U urban area). (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. How to get started with Application Modernization? 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. 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. Insurance Claims Risk Predictive Analytics and Software Tools. 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. (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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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? Figure 1: Sample of Health Insurance Dataset. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Here, our Machine Learning dashboard shows the claims types status. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The cost of claims based on health factors like BMI, age, smoker, health conditions and others building... May belong to a building without a fence had a slightly higher chance claiming! Conditions with accuracy is a major business metric for most of the training.. Fence had a slightly higher chance of claiming as compared to a outside... And severity of loss and severity of loss and severity of loss and of. Et al, two things are considered when analysing losses: frequency loss. 2016 ), neural network is very similar to biological neural Networks. ``, is... 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