December 1, 2025Journal of Innovation and TechnologyOpen Access

Study of RF and SVM Machine Learning Model to Predict Heart Disease

View Full Paper

Ask AI

Bookmark

Discussion

Member takes

Overview

Analysis finds 100% accuracy with Random Forest and 98.9% with Support Vector Machine for heart disease diagnosis, indicating improved patient outcomes.

Key Points

Random Forest achieved 100% accuracy in predicting heart disease, while Support Vector Machine reached 98.9%.
Machine learning methods analyzed complex patient data, enhancing traditional diagnostic approaches.
Evaluation integrates interpretability tools SHAP and LIME for clear clinical insights.
The findings suggest improved diagnosis of heart disease, aiming to reduce complications and mortality.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/69402a722d562116f2901e72 https://doi.org/10.61453/joit.v2025no19

View Full Paper

Ask AI

Bookmark

December 1, 2025Journal of Innovation and TechnologyOpen Access

Study of RF and SVM Machine Learning Model to Predict Heart Disease

View Full Paper

Ask AI

Bookmark

Discussion

Member takes

Overview

Analysis finds 100% accuracy with Random Forest and 98.9% with Support Vector Machine for heart disease diagnosis, indicating improved patient outcomes.

Key Points

Random Forest achieved 100% accuracy in predicting heart disease, while Support Vector Machine reached 98.9%.
Machine learning methods analyzed complex patient data, enhancing traditional diagnostic approaches.
Evaluation integrates interpretability tools SHAP and LIME for clear clinical insights.
The findings suggest improved diagnosis of heart disease, aiming to reduce complications and mortality.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/69402a722d562116f2901e72 https://doi.org/10.61453/joit.v2025no19

View Full Paper

Ask AI

Bookmark