For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. The effect of various independent variables on the premium amount was also checked. 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). During the training phase, the primary concern is the model selection. Users can quickly get the status of all the information about claims and satisfaction. 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 . In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. Health Insurance Claim Prediction Using Artificial Neural Networks. Each plan has its own predefined . "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). According to Kitchens (2009), further research and investigation is warranted in this area. In the past, research by Mahmoud et al. . An inpatient claim may cost up to 20 times more than an outpatient claim. The larger the train size, the better is the accuracy. needed. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. Health Insurance Cost Predicition. The model was used to predict the insurance amount which would be spent on their health. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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. 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. Currently utilizing existing or traditional methods of forecasting with variance. (2011) and El-said et al. 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. Your email address will not be published. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Are you sure you want to create this branch? for the project. 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. Dataset is not suited for the regression to take place directly. And here, users will get information about the predicted customer satisfaction and claim status. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The network was trained using immediate past 12 years of medical yearly claims data. 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. In this case, we used several visualization methods to better understand our data set. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Logs. All Rights Reserved. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. Approach : Pre . Multiple linear regression can be defined as extended simple linear regression. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. The model used the relation between the features and the label to predict the amount. In the next blog well explain how we were able to achieve this goal. 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. history Version 2 of 2. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. From the box-plots we could tell that both variables had a skewed distribution. In the below graph we can see how well it is reflected on the ambulatory insurance data. The topmost decision node corresponds to the best predictor in the tree called root node. 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! age : age of policyholder sex: gender of policy holder (female=0, male=1) Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? DATASET USED The primary source of data for this project was . According to Rizal et al. ). A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Continue exploring. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. The models can be applied to the data collected in coming years to predict the premium. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Numerical data along with categorical data can be handled by decision tress. Dong et al. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! Key Elements for a Successful Cloud Migration? (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. 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. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. arrow_right_alt. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. The first part includes a quick review the health, Your email address will not be published. Logs. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Dr. Akhilesh Das Gupta Institute of Technology & Management. So cleaning of dataset becomes important for using the data under various regression algorithms. Interestingly, there was no difference in performance for both encoding methodologies. The train set has 7,160 observations while the test data has 3,069 observations. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. A tag already exists with the provided branch name. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. (R rural area, U urban area). 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. (2022). 1 input and 0 output. trend was observed for the surgery data). 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. (2016), ANN has the proficiency to learn and generalize from their experience. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. can Streamline Data Operations and enable 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. of a health insurance. At the same time fraud in this industry is turning into a critical problem. 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. Decision on the numerical target is represented by leaf node. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Training data has one or more inputs and a desired output, called as a supervisory signal. The data included some ambiguous values which were needed to be removed. 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