These claim amounts are usually high in millions of dollars every year. The main application of unsupervised learning is density estimation in statistics. Settlement: Area where the building is located. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Other two regression models also gave good accuracies about 80% In their prediction. (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. for example). (2011) and El-said et al. A tag already exists with the provided branch name. 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 not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The real-world data is noisy, incomplete and inconsistent. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: A major cause of increased costs are payment errors made by the insurance companies while processing claims. Refresh the page, check. Each plan has its own predefined . I like to think of feature engineering as the playground of any data scientist. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. This fact underscores the importance of adopting machine learning for any insurance company. (2011) and El-said et al. Save my name, email, and website in this browser for the next time I comment. The attributes also in combination were checked for better accuracy results. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. J. Syst. Claim rate, however, is lower standing on just 3.04%. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. The authors Motlagh et al. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Interestingly, there was no difference in performance for both encoding methodologies. And its also not even the main issue. Abhigna et al. Dataset is not suited for the regression to take place directly. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. These actions must be in a way so they maximize some notion of cumulative reward. 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). This is the field you are asked to predict in the test set. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Fig. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. was the most common category, unfortunately). How to get started with Application Modernization? All Rights Reserved. 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. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. During the training phase, the primary concern is the model selection. Creativity and domain expertise come into play in this area. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. 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. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. However, training has to be done first with the data associated. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Are you sure you want to create this branch? This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Your email address will not be published. (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. To do this we used box plots. Dr. Akhilesh Das Gupta Institute of Technology & Management. Logs. Factors determining the amount of insurance vary from company to company. 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. (2022). 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. arrow_right_alt. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. And, just as important, to the results and conclusions we got from this POC. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 1993, Dans 1993) because these databases are designed for nancial . age : age of policyholder sex: gender of policy holder (female=0, male=1) The Company offers a building insurance that protects against damages caused by fire or vandalism. Goundar, Sam, et al. The data was imported using pandas library. 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. 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. 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. Using the final model, the test set was run and a prediction set obtained. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Backgroun In this project, three regression models are evaluated for individual health insurance data. The primary source of data for this project was from Kaggle user Dmarco. In I. (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 size of the data used for training of data has a huge impact on the accuracy of data. of a health insurance. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. All Rights Reserved. The data was in structured format and was stores in a csv file format. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2016), neural network is very similar to biological neural networks. 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. Dong et al. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Keywords Regression, Premium, Machine Learning. The larger the train size, the better is the accuracy. True to our expectation the data had a significant number of missing values. Dyn. These claim amounts are usually high in millions of dollars every year. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. By filtering and various machine learning models accuracy can be improved. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Also it can provide an idea about gaining extra benefits from the health insurance. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. (2020). 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. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 2 shows various machine learning types along with their properties. Required fields are marked *. Using this approach, a best model was derived with an accuracy of 0.79. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. arrow_right_alt. ), Goundar, Sam, et al. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. 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. Coders Packet . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Attributes which had no effect on the prediction were removed from the features. 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. 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. 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