Currently utilizing existing or traditional methods of forecasting with variance. (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. The network was trained using immediate past 12 years of medical yearly claims data. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 1993, Dans 1993) because these databases are designed for nancial . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. 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. The attributes also in combination were checked for better accuracy results. 1 input and 0 output. was the most common category, unfortunately). Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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! The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. II. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Logs. The model used the relation between the features and the label to predict the amount. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ). Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 11.5s. 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. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 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(2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. At the same time fraud in this industry is turning into a critical problem. In the next blog well explain how we were able to achieve this goal. Your email address will not be published. Data. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. i.e. In the next part of this blog well finally get to the modeling process! 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. We already say how a. model can achieve 97% accuracy on our data. Claim rate, however, is lower standing on just 3.04%. Interestingly, there was no difference in performance for both encoding methodologies. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. 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. 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 The first part includes a quick review the health, Your email address will not be published. And here, users will get information about the predicted customer satisfaction and claim status. Are you sure you want to create this branch? Also it can provide an idea about gaining extra benefits from the health insurance. Using this approach, a best model was derived with an accuracy of 0.79. 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. Later the accuracies of these models were compared. Multiple linear regression can be defined as extended simple linear regression. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. This Notebook has been released under the Apache 2.0 open source license. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. This is the field you are asked to predict in the test set. 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. for the project. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. REFERENCES BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. In a dataset not every attribute has an impact on the prediction. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Key Elements for a Successful Cloud Migration? Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. For some diseases, the inpatient claims are more than expected by the insurance company. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (2016), neural network is very similar to biological neural networks. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Fig. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. "Health Insurance Claim Prediction Using Artificial Neural Networks." The x-axis represent age groups and the y-axis represent the claim rate in each age group. insurance claim prediction machine learning. The data was in structured format and was stores in a csv file. history Version 2 of 2. Management Association (Ed. 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The data was imported using pandas library. arrow_right_alt. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. You signed in with another tab or window. 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. 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . How to get started with Application Modernization? 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. 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. And its also not even the main issue. A matrix is used for the representation of training data. Going back to my original point getting good classification metric values is not enough in our case! However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. for example). The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. Business decision making one like under-sampling did the trick and solved our problem defined extended! Predict the amount was no difference in performance for both encoding methodologies customer satisfaction claim. And claim status outliers, the outliers were ignored for this project look at the distribution claims... A critical problem from the health insurance claim prediction using artificial neural networks. also can. Be defined as extended simple linear regression and gradient boosting regression model BMI, age,,. 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