Comparing different supervised machine learning algorithms for disease prediction

Comparing different supervised machine learning algorithms for disease prediction

(2019) 19:281 | Shahadat Uddin, Arif Khan, Md Ekramul Hossain, Mohammad Ali Moni
This study aims to identify key trends and performance differences among various supervised machine learning algorithms for disease prediction. The research involved a comprehensive review of 48 articles that applied multiple supervised machine learning algorithms to predict different diseases. The Support Vector Machine (SVM) algorithm was the most frequently used (29 studies), followed by Naïve Bayes (23 studies). However, Random Forest (RF) showed superior accuracy, achieving the highest accuracy in 53% of the studies, compared to 41% for SVM. The study provides valuable insights into the relative performance of different algorithms, which can aid researchers in selecting appropriate methods for their studies. The findings highlight the potential of these algorithms in disease prediction, emphasizing the importance of choosing the right algorithm based on the specific disease and dataset characteristics.This study aims to identify key trends and performance differences among various supervised machine learning algorithms for disease prediction. The research involved a comprehensive review of 48 articles that applied multiple supervised machine learning algorithms to predict different diseases. The Support Vector Machine (SVM) algorithm was the most frequently used (29 studies), followed by Naïve Bayes (23 studies). However, Random Forest (RF) showed superior accuracy, achieving the highest accuracy in 53% of the studies, compared to 41% for SVM. The study provides valuable insights into the relative performance of different algorithms, which can aid researchers in selecting appropriate methods for their studies. The findings highlight the potential of these algorithms in disease prediction, emphasizing the importance of choosing the right algorithm based on the specific disease and dataset characteristics.
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