Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Dr. Akhilesh Das Gupta Institute of Technology & Management. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Other two regression models also gave good accuracies about 80% In their prediction. 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. Implementing a Kubernetes Strategy in Your Organization? Logs. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. Numerical data along with categorical data can be handled by decision tress. This Notebook has been released under the Apache 2.0 open source license. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. In the next part of this blog well finally get to the modeling process! You signed in with another tab or window. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Here, our Machine Learning dashboard shows the claims types status. The distribution of number of claims is: Both data sets have over 25 potential features. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. We already say how a. model can achieve 97% accuracy on our data. Your email address will not be published. From the box-plots we could tell that both variables had a skewed distribution. 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 data was imported using pandas library. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. The network was trained using immediate past 12 years of medical yearly claims data. Your email address will not be published. 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. 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. According to Zhang et al. (2020). Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. ), 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. 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. necessarily differentiating between various insurance plans). 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. 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Insurance companies are extremely interested in the prediction of the future. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. effective Management. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. The dataset is comprised of 1338 records with 6 attributes. (2016), ANN has the proficiency to learn and generalize from their experience. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Last modified January 29, 2019, Your email address will not be published. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. arrow_right_alt. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Neural networks can be distinguished into distinct types based on the architecture. The primary source of data for this project was from Kaggle user Dmarco. DATASET USED The primary source of data for this project was . In I. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. age : age of policyholder sex: gender of policy holder (female=0, male=1) With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. for the project. By filtering and various machine learning models accuracy can be improved. 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 Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Training data has one or more inputs and a desired output, called as a supervisory signal. In a dataset not every attribute has an impact on the prediction. 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 is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. 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. The diagnosis set is going to be expanded to include more diseases. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Leverage the True potential of AI-driven implementation to streamline the development of applications. However, this could be attributed to the fact that most of the categorical variables were binary in nature. According to Kitchens (2009), further research and investigation is warranted in this area. The mean and median work well with continuous variables while the Mode works well with categorical variables. Save my name, email, and website in this browser for the next time I comment. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Data. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. The topmost decision node corresponds to the best predictor in the tree called root node. Figure 1: Sample of Health Insurance Dataset. 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. 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. 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. (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. Fig. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Comments (7) Run. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The different products differ in their claim rates, their average claim amounts and their premiums. Health Insurance Claim Prediction Using Artificial Neural Networks. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. Example, Sangwan et al. Logs. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. (2011) and El-said et al. You signed in with another tab or window. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. As a result, the median was chosen to replace the missing values. To do this we used box plots. 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. Keywords Regression, Premium, Machine Learning. Dyn. 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)? 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. This may sound like a semantic difference, but its not. Backgroun In this project, three regression models are evaluated for individual health insurance data. Various factors were used and their effect on predicted amount was examined. Fig. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. 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. HEALTH_INSURANCE_CLAIM_PREDICTION. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Management Association (Ed. Introduction to Digital Platform Strategy? A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. This amount needs to be included in the yearly financial budgets. According to Rizal et al. 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. Settlement: Area where the building is located. ). The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Where a person can ensure that the amount he/she is going to opt is justified. The website provides with a variety of data and the data used for the project is an insurance amount data. Factors determining the amount of insurance vary from company to company. 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. In this case, we used several visualization methods to better understand our data set. 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. Decision on the numerical target is represented by leaf node. We see that the accuracy of predicted amount was seen best. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Description. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (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. Result, the median was chosen to replace the missing values correct claim amount has a significant impact on architecture. Analyse the personal health data to predict a correct claim amount has a significant impact on insurer 's decisions. 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