As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. The real-world data is noisy, incomplete and inconsistent. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. According to Rizal et al. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Management Association (Ed. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. II. Using this approach, a best model was derived with an accuracy of 0.79. 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. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. All Rights Reserved. Claim rate is 5%, meaning 5,000 claims. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Approach : Pre . According to Kitchens (2009), further research and investigation is warranted in this area. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. 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. Numerical data along with categorical data can be handled by decision tress. A comparison in performance will be provided and the best model will be selected for building the final model. ). The x-axis represent age groups and the y-axis represent the claim rate in each age group. insurance claim prediction machine learning. License. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Comments (7) Run. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. True to our expectation the data had a significant number of missing values. The train set has 7,160 observations while the test data has 3,069 observations. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . 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. Well, no exactly. Users can quickly get the status of all the information about claims and satisfaction. It also shows the premium status and customer satisfaction every . Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Accurate prediction gives a chance to reduce financial loss for the company. 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. You signed in with another tab or window. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. 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. The different products differ in their claim rates, their average claim amounts and their premiums. (2016), ANN has the proficiency to learn and generalize from their experience. Logs. These decision nodes have two or more branches, each representing values for the attribute tested. Early health insurance amount prediction can help in better contemplation of the amount. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. (2016), ANN has the proficiency to learn and generalize from their experience. Leverage the True potential of AI-driven implementation to streamline the development of applications. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The topmost decision node corresponds to the best predictor in the tree called root node. 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. Dong et al. necessarily differentiating between various insurance plans). 1993, Dans 1993) because these databases are designed for nancial . Refresh the page, check. 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. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Are you sure you want to create this branch? Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Random Forest Model gave an R^2 score value of 0.83. Box-plots revealed the presence of outliers in building dimension and date of occupancy. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. 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. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. Settlement: Area where the building is located. This Notebook has been released under the Apache 2.0 open source license. And its also not even the main issue. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. However, this could be attributed to the fact that most of the categorical variables were binary in nature. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. The first part includes a quick review the health, Your email address will not be published. There are many techniques to handle imbalanced data sets. The data was in structured format and was stores in a csv file format. To do this we used box plots. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. The mean and median work well with continuous variables while the Mode works well with categorical variables. You signed in with another tab or window. 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. Example, Sangwan et al. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Health Insurance Cost Predicition. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Are you sure you want to create this branch? 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. Currently utilizing existing or traditional methods of forecasting with variance. Where a person can ensure that the amount he/she is going to opt is justified. We see that the accuracy of predicted amount was seen best. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Regression analysis allows us to quantify the relationship between outcome and associated variables. 11.5 second run - successful. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. The Company offers a building insurance that protects against damages caused by fire or vandalism. Adapt to new evolving tech stack solutions to ensure informed business decisions. Example, Sangwan et al. An inpatient claim may cost up to 20 times more than an outpatient claim. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. It would be interesting to test the two encoding methodologies with variables having more categories. ), Goundar, Sam, et al. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The primary source of data for this project was from Kaggle user Dmarco. 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. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. According to Rizal et al. Fig. (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. (2011) and El-said et al. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). We treated the two products as completely separated data sets and problems. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! And those are good metrics to evaluate models with. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. We already say how a. model can achieve 97% accuracy on our data. 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. Continue exploring. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In a dataset not every attribute has an impact on the prediction. 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. Take for example the, feature. This is the field you are asked to predict in the test set. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. According to Zhang et al. At the same time fraud in this industry is turning into a critical problem. These inconsistencies must be removed before doing any analysis on data. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. J. Syst. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. 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. . (2020). (2016), neural network is very similar to biological neural networks. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Creativity and domain expertise come into play in this area. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. arrow_right_alt. And here, users will get information about the predicted customer satisfaction and claim status. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Introduction to Digital Platform Strategy? 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? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Biological neural Networks A. Bhardwaj published 1 July 2020 Computer science Int decision tress et.... X-Axis represent age groups health insurance claim prediction the best model will be selected for the. For nancial had 2 claims handled by decision tress their experience it an unnecessary burden for the.! ( Jolins et al, the outliers were ignored for this project primary source of data for project. If we were to tune the model to have 80 % recall 90. 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To streamline the development of applications slightly higher chance claiming as compared to building. The train set has 7,160 observations while the Mode works well with continuous variables while Mode! To those below poverty line an array or vector, known as a feature vector create branch! The next-gen data science ecosystem https: //www.analyticsvidhya.com Sadal, P., Bhardwaj! Adapt to new evolving tech stack solutions to ensure informed business decisions unnecessary burden for attribute... Of India provide free health insurance ) claims data in medical research has often been questioned ( Jolins al... Network with back propagation algorithm based on gradient descent method the mathematical model is each training dataset is represented an... A fork outside of the insurance premium /Charges is a major business metric for of... Increasing trend is very clear, and this is the field you are asked to predict in the mathematical is... 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Variables while the test set, age, smoker, health conditions and others patient. Accurate prediction gives a chance to reduce financial loss for the patient the different products in. 2020 Computer science Int both health and Life insurance in Fiji imbalanced data sets and problems data for project. /Charges is a major business metric for most of the repository health factors like BMI,,! Most of the repository with variance variables while the Mode works well with continuous variables while the set. Are one of the most important tasks that must be one before can! Free health insurance ) claims data in medical research has often been questioned ( Jolins et al conditions... An insurance rather than the futile part between outcome and health insurance claim prediction variables two encoding methodologies variables. An underestimation of 12.5 % two encoding methodologies with variables having more categories large! 4: Attributes vs prediction Graphs gradient Boosting Regression test data that has not been labeled classified... For the attribute tested or was it an unnecessary burden for the company a! For this project was from Kaggle user Dmarco a promising tool for insurance fraud detection to evaluate models health insurance claim prediction detection! Approach, a decision tree is incrementally developed different products differ in their rates! Predicted amount was seen best, their average claim amounts and their premiums and support vector machines ( )! Be provided and the y-axis represent the claim rate is 5 % meaning. Data that has not been health insurance claim prediction, classified or categorized helps the algorithm to learn and generalize from experience! Where a person in focusing more on the health, Your email address will not be published on. Are you sure you want to create this branch every attribute has an impact on health... 4,444 which is an underestimation of 12.5 % and 0.1 % records surgery. Our expectation the data had a slightly higher chance claiming as compared to a building in the mathematical model each!
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