# Medical Premium Prediction: Application of Machine Learning and Deep Learning in Insurance Pricing

> This article introduces a medical premium prediction project based on machine learning and deep learning, covering data preprocessing, feature engineering, model training and evaluation, as well as an interactive deployment solution implemented via Streamlit.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-25T20:15:25.000Z
- 最近活动: 2026-05-25T20:18:06.158Z
- 热度: 153.0
- 关键词: 医疗保费预测, 机器学习, 深度学习, 保险定价, 随机森林, XGBoost, 神经网络, Streamlit, 数据科学
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-msk-237-medical-insurance-cost-prediction
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-msk-237-medical-insurance-cost-prediction
- Markdown 来源: floors_fallback

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## Introduction: Core Overview of the Medical Premium Prediction Project

This article introduces the medical premium prediction project published by MSK-237 on GitHub. Using machine learning (Linear Regression, Random Forest, SVR, XGBoost) and deep learning (MLP) technologies, it implements the full workflow from data preprocessing to interactive deployment via Streamlit. The project aims to solve the problem that traditional premium pricing struggles to accurately capture individual risk differences, providing a data-driven solution for insurance pricing.

## Project Background and Significance

Medical premium pricing is a core issue in the insurance industry. Traditional methods rely on actuaries' empirical rules, which struggle to accurately capture individual risk differences. This project integrates machine learning and deep learning technologies to build prediction models from multiple dimensions such as age, gender, BMI, etc., helping insurance companies optimize risk assessment and providing consumers with a basis for transparent pricing.

## Dataset and Feature Engineering Processing

A classic medical premium dataset is used, containing features such as age, gender, BMI, number of children, smoking status, region, etc. In the feature engineering phase, one-hot encoding is used to process categorical variables, and numerical features are standardized to ensure the model effectively learns data patterns.

## Model Implementation: Comparison Between Machine Learning and Deep Learning

### Machine Learning Models
- Linear Regression: Baseline model, assumes linear relationships, strong interpretability
- Random Forest: Ensemble of decision trees, reduces overfitting, captures non-linear interactions
- SVR: Kernel function maps to high-dimensional space, optimizes parameters to handle complex relationships
- XGBoost: Gradient boosting algorithm, iteratively optimizes residuals, supports feature importance analysis

### Deep Learning Model
- Architecture: Multi-Layer Perceptron (MLP), input layer + 2 hidden layers (ReLU activation) + output layer (linear activation)
- Training Strategy: MSE loss function, Adam optimizer (learning rate 0.001), batch size 32, 200 epochs + early stopping
- Regularization: Dropout (0.3) + L2 regularization to improve generalization ability

## Model Evaluation Results and Feature Importance Analysis

Evaluation metrics include R², MSE, and MAE. Results: XGBoost (R²=0.88) performs best, followed by Random Forest (0.86), Neural Network (0.85) is close to tree models, and Linear Regression (0.78) serves as the baseline. Feature importance: Smoking status is the most critical, followed by age and BMI.

## Streamlit Interactive Deployment Solution

### User Interface Design
Input personal information (age, gender, BMI, etc.) via the Streamlit sidebar, and real-time display of prediction results and feature sensitivity analysis.

### Deployment Process
1. Install dependencies: `pip install -r requirements.txt`
2. Launch the application: `streamlit run app.py`
3. Access the local address to use; non-technical personnel can easily experience it.

## Practical Application Value and Future Outlook

### Application Value
- Risk Segmentation: Precisely identify high-risk and low-risk customers
- Pricing Fairness: Reduce subjective bias
- Customer Experience: Enhance pricing transparency and trust

### Future Improvements
- Introduce more features (past medical history, occupational risks)
- Try advanced models (tabular deep learning models like TabNet)
- Explore federated learning to achieve multi-institution data collaboration under privacy protection