Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

• DOI: 10.31893/multiscience.2024ss0601
• Publication Date: 2024-08-19T16:50:33Z
• Authors: Kavina Ganapathy; Ekta Kapoor; Devanshu J. Patel; V. K. Singh

Heart disease is a leading cause of death worldwide, becoming major health concern for many people. Among the most important tasks and regular diagnostic research entails identified cardiac issues, like heart diseases, valve conditions, etc. Early detection may save lives. The application machine learning techniques in medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented proposed work predicting disease. For this suggested investigation, information using individual customer identification at University California Irvine (UCI) utilized, data mining methods categorization were applied. Min–max normalization principal component analysis (PCA) used preparation feature extraction. As result, rather basic supervised technique be to predict quite accurately with excellent potential value. According study, classification based on RF produced results 99.45% accuracy, 98.3% recall, 98.6% precision, 98.9% f1 score. This finding demonstrates that our system more efficient than other cutting-edge techniques.