Machine Learning-based Lung Cancer Risk Prediction System: Multi-model Comparison and Early Diagnosis Application

This article introduces the open-source lung cancer risk prediction project developed by Yanne0800 (GitHub link: https://github.com/Yanne0800/Lung_Cancer_Prediction, released on June 15, 2026). The project integrates multi-dimensional patient data and uses multiple machine learning algorithms including Random Forest, Logistic Regression, and Support Vector Machine to build a complete early lung cancer risk prediction system. It aims to achieve accurate prediction and early intervention, with both clinical value and social significance.

Lung cancer is one of the malignant tumors with the highest incidence and mortality rates globally. Early detection can significantly improve survival rates. Traditional low-dose CT screening is costly and difficult to popularize, so developing machine learning-based risk prediction tools is crucial. This project integrates feature data such as age, smoking habits, and symptoms to help medical staff quickly identify high-risk groups, provide personalized assessments, and achieve early detection and intervention.

The project uses three classic machine learning algorithms for comparative experiments:

1. Random Forest: An ensemble learning method with strong ability to handle high-dimensional data and avoid overfitting, used as the main prediction model;
2. Logistic Regression: A binary classification model with high interpretability, which can clarify feature weights and help understand risk-influencing factors;
3. Support Vector Machine (SVM): With kernel functions, it captures complex patterns, has strong generalization ability, and performs well in classifying boundary samples.

Data processing steps include:

• Data cleaning: Handle missing values (similar sample imputation), outliers (medical knowledge correction/removal), and duplicate records;
• Feature selection: Cover demographics (age, gender), lifestyle (smoking years, drinking), symptoms (cough, hemoptysis, etc.), environmental factors (secondhand smoke, air pollution), and family history (immediate family members' lung cancer history);
• Data standardization: Numerical features are normalized to mean 0 and standard deviation 1 to meet the requirements of algorithms like SVM.

Training and evaluation strategies:

• Training: Split into training/test sets at an 8:2 ratio, use cross-validation to evaluate generalization ability, and grid search to optimize hyperparameters;
• Evaluation metrics: Accuracy, precision, recall, F1 score, ROC-AUC value, and confusion matrix;
• Visualization: Feature importance ranking, ROC curve comparison, confusion matrix heatmap, etc., to help understand the model logic.

System application scenarios:

1. Clinical auxiliary diagnosis: Quickly provide risk assessment to assist doctors in screening high-risk patients who need further examination;
2. Health checkup centers: Stratify the risk of examinees, prioritize CT examinations for high-risk groups, and optimize resource allocation;
3. Public health monitoring: Identify high-incidence trends in regions/populations to provide data support for public health policy formulation.

Core features of the project:

1. Multi-model comparison: No reliance on a single algorithm, select the optimal solution;
2. Complete ML workflow: Cover the full lifecycle from data preprocessing to model deployment;
3. High interpretability: Provide feature importance analysis to explain the basis of predictions;
4. Easy to extend: Clear code structure, convenient to add new features or algorithms.

This project demonstrates the potential of machine learning in the medical field. By integrating multi-source data and algorithms, it builds a practical and interpretable prediction system. In the future, with data accumulation and algorithm optimization, it is expected to become a standard component of early lung cancer screening. Meanwhile, this project is also an excellent learning resource covering the complete data science workflow, suitable for developers at all stages to learn and practice.