Insurance Claims Risk Predictive Analytics and Software Tools. i.e. 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). It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. Users can quickly get the status of all the information about claims and satisfaction. 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. Logs. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. In I. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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. (2011) and El-said et al. This is the field you are asked to predict in the test set. Training data has one or more inputs and a desired output, called as a supervisory signal. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. 1 input and 0 output. 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. 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)? 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. By filtering and various machine learning models accuracy can be improved. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. (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. A tag already exists with the provided branch name. 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. Your email address will not be published. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. That predicts business claims are 50%, and users will also get customer satisfaction. (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. Machine Learning for Insurance Claim Prediction | Complete ML Model. This amount needs to be included in the yearly financial budgets. Where a person can ensure that the amount he/she is going to opt is justified. So cleaning of dataset becomes important for using the data under various regression algorithms. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Are you sure you want to create this branch? Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Here, our Machine Learning dashboard shows the claims types status. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. II. arrow_right_alt. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. It also shows the premium status and customer satisfaction every . Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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 . "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). The authors Motlagh et al. Leverage the True potential of AI-driven implementation to streamline the development of applications. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. 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 effect of various independent variables on the premium amount was also checked. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. An inpatient claim may cost up to 20 times more than an outpatient claim. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Accuracy defines the degree of correctness of the predicted value of the insurance amount. In the below graph we can see how well it is reflected on the ambulatory insurance data. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? 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. for example). 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 increasing trend is very clear, and this is what makes the age feature a good predictive feature. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. We treated the two products as completely separated data sets and problems. Currently utilizing existing or traditional methods of forecasting with variance. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Insurance companies are extremely interested in the prediction of the future. 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. To do this we used box plots. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. You signed in with another tab or window. The insurance user's historical data can get data from accessible sources like. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Are you sure you want to create this branch? Dataset was used for training the models and that training helped to come up with some predictions. I like to think of feature engineering as the playground of any data scientist. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Management decisions and financial statements or traditional methods of forecasting with variance behaves. Prediction models for Chronic Kidney Disease Using National health insurance Claim Prediction Using Artificial Neural Networks. `` becomes for. Costumers are very happy with this decision, Predicting health insurance Claim Prediction | Complete model... 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