Related Experiment Video
Updated: Sep 5, 2025
07:31
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.2K
Machine Learning-Based Regression Framework to Predict Health Insurance Premiums
Keshav Kaushik1, Akashdeep Bhardwaj1, Ashutosh Dhar Dwivedi2
1School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India.
Summary
Artificial intelligence (AI) and machine learning (ML) accurately predict health insurance premiums using patient data. This technology enhances efficiency and accuracy in health insurance, benefiting both insurers and policyholders.
Area of Science:
- Health Informatics
- Artificial Intelligence
- Machine Learning
Background:
- Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by improving disease prediction and diagnosis.
- Digital health insurance, powered by AI/ML, reduces the gap between insurers and consumers, streamlining services.
- AI/ML enable insurers to create more accurate and efficient health insurance policies.
Purpose of the Study:
- To train and evaluate an AI network-based regression model for predicting health insurance premiums.
- To forecast individual health insurance costs based on specific user features.
- To assess the model's performance using key performance metrics.
Main Methods:
- An artificial neural network model was developed and trained.
- The model utilized various parameters including age, gender, BMI, number of children, smoking habits, and geolocation.
- The model's accuracy and performance were evaluated using established metrics.
Main Results:
- The AI model achieved a prediction accuracy of 92.72%.
- The study demonstrated the feasibility of using AI for precise health insurance premium calculation.
- Performance analysis confirmed the model's effectiveness in predicting insurance costs.
Conclusions:
- AI and ML models can accurately predict health insurance premiums.
- The developed model offers a data-driven approach to health insurance cost estimation.
- This technology has the potential to enhance the efficiency and accuracy of the health insurance industry.
Related Concept Videos
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:11Regression Analysis
6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
01:25Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
01:11Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
01:20Actuarial Approach
130
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
130
01:52Regression Toward the Mean
6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K