This study compares the performance of different supervised machine learning algorithms for disease prediction. It analyzed 48 articles that applied multiple supervised machine learning algorithms for single disease prediction. The most frequently used algorithms were Support Vector Machine (SVM) in 29 studies and Naïve Bayes in 23 studies. However, Random Forest (RF) showed superior accuracy in 53% of the studies, followed by SVM in 41% of the studies. The study found that RF had the highest accuracy in 9 out of 17 studies where it was applied. The results indicate that RF outperforms other algorithms in disease risk prediction. The study also highlights the advantages and limitations of different supervised machine learning algorithms. The findings suggest that RF is the most effective algorithm for disease prediction, followed by SVM. The study provides a comprehensive overview of the relative performance of different supervised machine learning algorithms for disease prediction, which can help researchers select the most appropriate algorithm for their studies. The study also emphasizes the importance of using multiple supervised machine learning algorithms for disease prediction to ensure accurate and reliable results. The results show that the performance of different algorithms varies depending on the disease and the data used. The study concludes that supervised machine learning algorithms have significant potential in disease prediction and that further research is needed to improve their accuracy and effectiveness.This study compares the performance of different supervised machine learning algorithms for disease prediction. It analyzed 48 articles that applied multiple supervised machine learning algorithms for single disease prediction. The most frequently used algorithms were Support Vector Machine (SVM) in 29 studies and Naïve Bayes in 23 studies. However, Random Forest (RF) showed superior accuracy in 53% of the studies, followed by SVM in 41% of the studies. The study found that RF had the highest accuracy in 9 out of 17 studies where it was applied. The results indicate that RF outperforms other algorithms in disease risk prediction. The study also highlights the advantages and limitations of different supervised machine learning algorithms. The findings suggest that RF is the most effective algorithm for disease prediction, followed by SVM. The study provides a comprehensive overview of the relative performance of different supervised machine learning algorithms for disease prediction, which can help researchers select the most appropriate algorithm for their studies. The study also emphasizes the importance of using multiple supervised machine learning algorithms for disease prediction to ensure accurate and reliable results. The results show that the performance of different algorithms varies depending on the disease and the data used. The study concludes that supervised machine learning algorithms have significant potential in disease prediction and that further research is needed to improve their accuracy and effectiveness.