This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. License. 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. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). Your email address will not be published. Here, our Machine Learning dashboard shows the claims types status. By filtering and various machine learning models accuracy can be improved. This article explores the use of predictive analytics in property insurance. for the project. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. So cleaning of dataset becomes important for using the data under various regression algorithms. Leverage the True potential of AI-driven implementation to streamline the development of applications. We see that the accuracy of predicted amount was seen best. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. (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. Example, Sangwan et al. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Neural networks can be distinguished into distinct types based on the architecture. "Health Insurance Claim Prediction Using Artificial Neural Networks.". One of the issues is the misuse of the medical insurance systems. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 99.5% in gradient boosting decision tree regression. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. of a health insurance. (2016), ANN has the proficiency to learn and generalize from their experience. Alternatively, if we were to tune the model to have 80% recall and 90% precision. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Early health insurance amount prediction can help in better contemplation of the amount needed. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Key Elements for a Successful Cloud Migration? Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. In the next blog well explain how we were able to achieve this goal. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. There are many techniques to handle imbalanced data sets. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 1. All Rights Reserved. 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Required fields are marked *. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Machine Learning approach is also used for predicting high-cost expenditures in health care. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. At the same time fraud in this industry is turning into a critical problem. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. This Notebook has been released under the Apache 2.0 open source license. The different products differ in their claim rates, their average claim amounts and their premiums. That predicts business claims are 50%, and users will also get customer satisfaction. "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). Training data has one or more inputs and a desired output, called as a supervisory signal. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. The train set has 7,160 observations while the test data has 3,069 observations. The primary source of data for this project was from Kaggle user Dmarco. II. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. A tag already exists with the provided branch name. In the past, research by Mahmoud et al. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Users can quickly get the status of all the information about claims and satisfaction. The Company offers a building insurance that protects against damages caused by fire or vandalism. The data was in structured format and was stores in a csv file format. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. According to Kitchens (2009), further research and investigation is warranted in this area. 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. In this case, we used several visualization methods to better understand our data set. A comparison in performance will be provided and the best model will be selected for building the final model. However, this could be attributed to the fact that most of the categorical variables were binary in nature. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Other two regression models also gave good accuracies about 80% In their prediction. However, it is. Here, our Machine Learning dashboard shows the claims types status. Numerical data along with categorical data can be handled by decision tress. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 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. Dong et al. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Using the final model, the test set was run and a prediction set obtained. So, without any further ado lets dive in to part I ! The data was in structured format and was stores in a csv file. 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. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. Private health insurance training and testing phase of the categorical variables were binary in nature predicted amount seen! Disease using National health insurance is a necessity nowadays, and almost every individual is with... Terms and conditions the medical insurance systems area had a slightly higher chance claiming as compared a... 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