Journal of Clinical Medicine Research

Objective: To explore the application effectiveness of machine learning techniques in early disease diagnosis, compare different algorithms, and provide optimization references for disease classification tasks. Methods: A simulated dataset with 1,000 samples, including age, biomarker concentrations, and imaging features, was constructed. Logistic regression, random forest, and support vector machine (SVM) algorithms were employed for experiments. Model performance was evaluated using three metrics: accuracy, sensitivity, and specificity. Results: The random forest model outperformed others across all metrics (accuracy: 92.7%, sensitivity: 90.5%, specificity: 94.8%). The SVM achieved high specificity (92.9%) but slightly lower sensitivity (85.6%). Logistic regression demonstrated lower performance but was suitable for rapid diagnostic scenarios. Conclusion: Random forest is well-suited for diagnosing diseases with complex, nonlinear features, while SVM and logistic regression have utility in specific tasks. Future work may focus on integrating deep learning and multimodal data to further optimize diagnostic performance.