health insurance claim predictionhealth insurance claim prediction

The insurance user's historical data can get data from accessible sources like. 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 . 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. The main application of unsupervised learning is density estimation in statistics. age : age of policyholder sex: gender of policy holder (female=0, male=1) Dataset was used for training the models and that training helped to come up with some predictions. necessarily differentiating between various insurance plans). 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. 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. Keywords Regression, Premium, Machine Learning. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. The website provides with a variety of data and the data used for the project is an insurance amount data. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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%. 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. All Rights Reserved. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. It would be interesting to see how deep learning models would perform against the classic ensemble methods. A tag already exists with the provided branch name. Health Insurance Claim Prediction Using Artificial Neural Networks. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! Using this approach, a best model was derived with an accuracy of 0.79. 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. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. The data was imported using pandas library. In the next blog well explain how we were able to achieve this goal. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The models can be applied to the data collected in coming years to predict the premium. 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. 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 . Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. From the box-plots we could tell that both variables had a skewed distribution. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Users can develop insurance claims prediction models with the help of intuitive model visualization tools. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Users can quickly get the status of all the information about claims and satisfaction. 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. The network was trained using immediate past 12 years of medical yearly claims data. These decision nodes have two or more branches, each representing values for the attribute tested. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Using the final model, the test set was run and a prediction set obtained. Abhigna et al. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Application and deployment of insurance risk models . Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Take for example the, feature. 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. By filtering and various machine learning models accuracy can be improved. Also with the characteristics we have to identify if the person will make a health insurance claim. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. (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. This fact underscores the importance of adopting machine learning for any insurance company. Claim rate, however, is lower standing on just 3.04%. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. (2022). The effect of various independent variables on the premium amount was also checked. 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. You signed in with another tab or window. The diagnosis set is going to be expanded to include more diseases. This amount needs to be included in the yearly financial budgets. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. And its also not even the main issue. The real-world data is noisy, incomplete and inconsistent. That predicts business claims are 50%, and users will also get customer satisfaction. Coders Packet . Where a person can ensure that the amount he/she is going to opt is justified. 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 fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. Health Insurance Cost Predicition. Save my name, email, and website in this browser for the next time I comment. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2020). 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. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. 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. 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Fig. However, it is. This amount needs to be included in 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. These claim amounts are usually high in millions of dollars every year. (2016), neural network is very similar to biological neural networks. All Rights Reserved. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Attributes which had no effect on the prediction were removed from the features. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Leverage the True potential of AI-driven implementation to streamline the development of applications. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model.

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