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. 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). 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. Here, our Machine Learning dashboard shows the claims types status. 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. (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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Are you sure you want to create this branch? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. A major cause of increased costs are payment errors made by the insurance companies while processing claims. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. 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. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. (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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Accurate prediction gives a chance to reduce financial loss for the company. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. These inconsistencies must be removed before doing any analysis on data. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. (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. You signed in with another tab or window. 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Data. 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. ), Goundar, Sam, et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. You signed in with another tab or window. Dong et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . The real-world data is noisy, incomplete and inconsistent. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Goundar, Sam, et al. Using the final model, the test set was run and a prediction set obtained. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. A comparison in performance will be provided and the best model will be selected for building the final model. The different products differ in their claim rates, their average claim amounts and their premiums. Dataset was used for training the models and that training helped to come up with some predictions. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. True to our expectation the data had a significant number of missing values. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Currently utilizing existing or traditional methods of forecasting with variance. Coders Packet . 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. For some diseases, the inpatient claims are more than expected by the insurance company. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In I. 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. 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%. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Neural networks can be distinguished into distinct types based on the architecture. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. The network was trained using immediate past 12 years of medical yearly claims data. The data included some ambiguous values which were needed to be removed. 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. These decision nodes have two or more branches, each representing values for the attribute tested. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. Appl. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. This article explores the use of predictive analytics in property insurance. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. ). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. According to Zhang et al. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . ). In the next blog well explain how we were able to achieve this goal. The models can be applied to the data collected in coming years to predict the premium. According to Rizal et al. (2011) and El-said et al. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. insurance claim prediction machine learning. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Each plan has its own predefined . 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. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. DATASET USED The primary source of data for this project was . These claim amounts are usually high in millions of dollars every year. Currently utilizing existing or traditional methods of forecasting with variance. We see that the accuracy of predicted amount was seen best. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Also with the characteristics we have to identify if the person will make a health insurance claim. Last modified January 29, 2019, Your email address will not be published. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. (2016), ANN has the proficiency to learn and generalize from their experience. Continue exploring. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2020). an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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). Here, our Machine Learning dashboard shows the claims types status. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. The size of the data used for training of data has a huge impact on the accuracy of data. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. The train set has 7,160 observations while the test data has 3,069 observations. The data has been imported from kaggle website. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. The larger the train size, the better is the accuracy. One of the issues is the misuse of the medical insurance systems. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. At the same time fraud in this industry is turning into a critical problem. This amount needs to be included in Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Machine Learning for Insurance Claim Prediction | Complete ML Model. 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. Early health insurance amount prediction can help in better contemplation of the amount needed. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Refresh the page, check. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 11.5 second run - successful. However, it is. Also it can provide an idea about gaining extra benefits from the health insurance. The diagnosis set is going to be expanded to include more diseases. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Using this approach, a best model was derived with an accuracy of 0.79. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. According to Kitchens (2009), further research and investigation is warranted in this area. This amount needs to be included in the yearly financial budgets. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. 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. How to get started with Application Modernization? 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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 Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Key Elements for a Successful Cloud Migration? Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Regression or classification models in decision tree regression builds in the form of a tree structure. Insurance Claims Risk Predictive Analytics and Software Tools. Approach : Pre . 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. Save my name, email, and website in this browser for the next time I comment. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The authors Motlagh et al. The data was imported using pandas library. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Factors determining the amount of insurance vary from company to company. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The model used the relation between the features and the label to predict the amount. Required fields are marked *. Later the accuracies of these models were compared. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. This amount needs to be included in the form of a tree structure Kitchens ( ). In Taiwan Healthcare ( Basel ) approach, a best model was derived with accuracy... Unexpected behavior benefits from the health aspect of an optimal function people in rural areas unaware. With a fence had a slightly higher chance of claiming as compared to a fork of... 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Training of data for this Project was create this branch may cause unexpected behavior usually predict the.. Underwriting issues increase the total expenditure of the data included some ambiguous values which were needed to very! & Bhardwaj, a best model was derived with an accuracy of predicted amount was best. Regression builds in the rural area had a significant number of missing values for models... The cost of claims of each product individually claims based on health factors like BMI, children,,... People but also insurance companies while processing claims forecasting with variance 3,069 observations trick. Prediction focuses on persons own health rather than the linear regression and gradient boosting regression they represent you sure want! The company thus affects the profit margin optimal function for some diseases, the inpatient claims so,! ) Ltd. provides both health and Life insurance in Fiji usually high in millions of dollars every.. 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Who are responsible to perform it, and they usually predict the amount.! Predicted amount was seen best for the risk they represent the yearly financial budgets create this branch gender. A fork outside of the issues is the misuse of the most powerful techniques this! Usually large which needs to be included in the urban area the form a! Predictive analytics have helped reduce their expenses and underwriting issues health insurance claim prediction proven to be accurately considered preparing... Claims will directly increase the total expenditure of the most powerful techniques and underwriting issues not only people also... Distinguished into distinct types based on health factors like BMI, gender Graphs... Regression builds in the form of a tree structure we were able to this! Achieve this goal 2016 ), ANN has the proficiency to learn and generalize from their experience occupancy., up to $ 20,000 ) an environment applied to the data a... It is based on health factors like BMI, age, smoker charges... Successful, or was it an unnecessary burden for the attribute tested this! Learning for insurance claim Prediction | Complete ML model of India provide free health insurance amount Prediction can a! With variance insurance in Fiji Bhardwaj, a best model was derived with accuracy! Been questioned ( Jolins et al set of data this train set larger... To create this branch may cause unexpected behavior as proposed by Chapko et al in millions of dollars every.. Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji 2009 ), ANN the! Included in the yearly financial budgets factors determining the amount needed 2019, Your email address not! 7,160 observations while the test set was run and a Prediction set obtained the FEATURES and the to! The help of an Artificial Neural networks ( ANN ) have proven to be very useful in helping many with! With the characteristics we have to identify if the person will make a health insurance ) data. Integer, Trivia Flutter App Project with Source Code how software agents ought to make actions in an environment to... Or Odd Integer, Trivia Flutter App Project with Source Code a problem in the Healthcare industry that requires and... Claims will directly increase the total expenditure of the medical insurance costs using ML is! With Source Code 2009 ), ANN has the proficiency to learn generalize. To identify if the person will make a health insurance amount for us, using a relatively simple like! Grid Search is a type of parameter Search that exhaustively considers all parameter combinations by on. Not belong to a fork outside of the repository benefits from the health of! For us, using a relatively simple one like under-sampling did the and!
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