At the same time fraud in this industry is turning into a critical problem. 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. 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. 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. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! Comments (7) Run. 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 . Insurance companies are extremely interested in the prediction of the future. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. In this case, we used several visualization methods to better understand our data set. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Approach : Pre . 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. The data was imported using pandas library. 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. This fact underscores the importance of adopting machine learning for any insurance company. Various factors were used and their effect on predicted amount was examined. Are you sure you want to create this branch? Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. (2016), neural network is very similar to biological neural networks. 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. Currently utilizing existing or traditional methods of forecasting with variance. The data included some ambiguous values which were needed to be removed. The main application of unsupervised learning is density estimation in statistics. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. The size of the data used for training of data has a huge impact on the accuracy of data. And its also not even the main issue. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Well, no exactly. . https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. In I. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. There are many techniques to handle imbalanced data sets. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . age : age of policyholder sex: gender of policy holder (female=0, male=1) Dong et al. insurance claim prediction machine learning. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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. Regression analysis allows us to quantify the relationship between outcome and associated variables. Fig. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Application and deployment of insurance risk models . Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. How can enterprises effectively Adopt DevSecOps? for example). However, it is. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Factors determining the amount of insurance vary from company to company. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. J. Syst. II. A matrix is used for the representation of training data. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Description. Required fields are marked *. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. To do this we used box plots. In a dataset not every attribute has an impact on the prediction. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. REFERENCES Training data has one or more inputs and a desired output, called as a supervisory signal. This is the field you are asked to predict in the test set. The authors Motlagh et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. A comparison in performance will be provided and the best model will be selected for building the final model. Data. 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. The real-world data is noisy, incomplete and inconsistent. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. We treated the two products as completely separated data sets and problems. Accurate prediction gives a chance to reduce financial loss for the company. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 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. A tag already exists with the provided branch name. 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. "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). Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 1 input and 0 output. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (R rural area, U urban area). The final model was obtained using Grid Search Cross Validation. 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. 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. True to our expectation the data had a significant number of missing values. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. Are you sure you want to create this branch? needed. 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. ), Goundar, Sam, et al. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. The models can be applied to the data collected in coming years to predict the premium. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Neural networks can be distinguished into distinct types based on the architecture. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Keywords Regression, Premium, Machine Learning. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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. (2022). So, without any further ado lets dive in to part I ! It would be interesting to test the two encoding methodologies with variables having more categories. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. A major cause of increased costs are payment errors made by the insurance companies while processing claims. A decision tree with decision nodes and leaf nodes is obtained as a final result. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. The Company offers a building insurance that protects against damages caused by fire or vandalism. These actions must be in a way so they maximize some notion of cumulative reward. 1993, Dans 1993) because these databases are designed for nancial . According to Kitchens (2009), further research and investigation is warranted in this area. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. (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. The data has been imported from kaggle website. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. This sounds like a straight forward regression task!. Example, Sangwan et al. Implementing a Kubernetes Strategy in Your Organization? Logs. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Techniques for analyzing and predicting health insurance Part I smoker and charges as shown in fig and.. 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