The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 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. According to Rizal et al. (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. In a dataset not every attribute has an impact on the prediction. We see that the accuracy of predicted amount was seen best. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (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. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. The diagnosis set is going to be expanded to include more diseases. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Health Insurance Claim Prediction Using Artificial Neural Networks. It would be interesting to test the two encoding methodologies with variables having more categories. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Insurance companies are extremely interested in the prediction of the future. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Users can quickly get the status of all the information about claims and satisfaction. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. 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 . needed. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Random Forest Model gave an R^2 score value of 0.83. Dataset is not suited for the regression to take place directly. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. 11.5s. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Notebook. Well, no exactly. And those are good metrics to evaluate models with. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. However, it is. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. This amount needs to be included in Training data has one or more inputs and a desired output, called as a supervisory signal. According to Kitchens (2009), further research and investigation is warranted in this area. 1993, Dans 1993) because these databases are designed for nancial . Regression analysis allows us to quantify the relationship between outcome and associated variables. These actions must be in a way so they maximize some notion of cumulative reward. It also shows the premium status and customer satisfaction every . 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 building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Your email address will not be published. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. The authors Motlagh et al. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. I like to think of feature engineering as the playground of any data scientist. HEALTH_INSURANCE_CLAIM_PREDICTION. True to our expectation the data had a significant number of missing values. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. REFERENCES We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. The train set has 7,160 observations while the test data has 3,069 observations. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. 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. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Neural networks can be distinguished into distinct types based on the architecture. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Data. Dataset was used for training the models and that training helped to come up with some predictions. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Management Association (Ed. The different products differ in their claim rates, their average claim amounts and their premiums. ). 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 . These decision nodes have two or more branches, each representing values for the attribute tested. Using this approach, a best model was derived with an accuracy of 0.79. Are you sure you want to create this branch? In the next part of this blog well finally get to the modeling process! 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. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Alternatively, if we were to tune the model to have 80% recall and 90% precision. DATASET USED The primary source of data for this project was . Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Data. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Implementing a Kubernetes Strategy in Your Organization? The data was imported using pandas library. (R rural area, U urban area). Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. The network was trained using immediate past 12 years of medical yearly claims data. J. Syst. These claim amounts are usually high in millions of dollars every year. A decision tree with decision nodes and leaf nodes is obtained as a final result. This may sound like a semantic difference, but its not. Fig. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Your email address will not be published. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Fig. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Multiple linear regression can be defined as extended simple linear regression. "Health Insurance Claim Prediction Using Artificial Neural Networks.". (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Model performance was compared using k-fold cross validation. 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. insurance claim prediction machine learning. 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. 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%. So cleaning of dataset becomes important for using the data under various regression algorithms. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Those setting fit a Poisson regression problem. Good classifier, but its not those are good metrics to evaluate models with perform it, and they predict. 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