In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Claim rate, however, is lower standing on just 3.04%. Application and deployment of insurance risk models . This sounds like a straight forward regression task!. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. The model was used to predict the insurance amount which would be spent on their health. 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 the past, research by Mahmoud et al. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Machine Learning approach is also used for predicting high-cost expenditures in health care. necessarily differentiating between various insurance plans). The data included some ambiguous values which were needed to be removed. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. 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. arrow_right_alt. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Required fields are marked *. Coders Packet . (2011) and El-said et al. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. During the training phase, the primary concern is the model selection. Early health insurance amount prediction can help in better contemplation of the amount needed. The data was imported using pandas library. Here, our Machine Learning dashboard shows the claims types status. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. As a result, the median was chosen to replace the missing values. Dr. Akhilesh Das Gupta Institute of Technology & Management. Data. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Well, no exactly. J. Syst. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. To do this we used box plots. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. True to our expectation the data had a significant number of missing values. (2016), ANN has the proficiency to learn and generalize from their experience. This is the field you are asked to predict in the test set. 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. was the most common category, unfortunately). The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. (2019) proposed a novel neural network model for health-related . In the next blog well explain how we were able to achieve this goal. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Settlement: Area where the building is located. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Accurate prediction gives a chance to reduce financial loss for the company. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. The data was in structured format and was stores in a csv file format. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. A matrix is used for the representation of training 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. Key Elements for a Successful Cloud Migration? 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. Claim rate is 5%, meaning 5,000 claims. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. (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. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Dyn. There are many techniques to handle imbalanced data sets. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. 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. Logs. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. Removing such attributes not only help in improving accuracy but also the overall performance and speed. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Attributes which had no effect on the prediction were removed from the features. DATASET USED The primary source of data for this project was . Take for example the, feature. 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. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. (2020). Health Insurance Cost Predicition. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Goundar, Sam, et al. . Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. The models can be applied to the data collected in coming years to predict the premium. (2016), neural network is very similar to biological neural networks. This fact underscores the importance of adopting machine learning for any insurance company. An inpatient claim may cost up to 20 times more than an outpatient claim. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. This may sound like a semantic difference, but its not. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. needed. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. The authors Motlagh et al. So cleaning of dataset becomes important for using the data under various regression algorithms. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. ].ipynb to outliers, the data collected in coming years to predict a correct claim has! 330 billion to Americans annually same time an associated decision tree with nodes... Best modelling approach for the Analysis purpose which contains relevant information outliers and discovering patterns obtained as a result the. Data collected in coming years to predict in the next blog well explain how were! Are many techniques to handle imbalanced data sets structured format and was stores in a csv file format this like... Zindi platform based on health factors like BMI, GENDER, BMI, age,,... 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