ArticleBMC medical informatics and decision making2019
Comparing different supervised machine learning algorithms for disease prediction.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 531 papers, 6 of them syntheses that pooled it.
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Who cites it
531 citing papers in PubMed, 6 syntheses or guidelines pooled it.
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- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Machine learning-based myocardial infarction bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Dynamic flexibility of the murine gut microbiota during morphine disturbance enables escape from the stable dysbiosis that is associated with addiction-like behavior.Gut microbes · 2026Article
- Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.Renal failure · 2026Article
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- Development of an Interpretable Triage Tool for Colorectal Polyp Risk Stratification Within a Population-Based Screening Program: Machine Learning Approach.JMIR medical informatics · 2026Article
- Limited Predictability of Traumatic Intracranial Hemorrhage from Routine Pre-CT Clinical Variables in Older Adults with Low-Energy Falls: A Systematic Benchmarking Study in a Retrospective Bicentric Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer.International journal of cancer · 2026Article
- Discovering potential key biomarkers and molecular mechanisms in Chronic Obstructive Pulmonary Disease and rheumatoid arthritis using integrated bioinformatics and machine learning approaches.Journal, genetic engineering & biotechnology · 2026Article
- Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.PLOS digital health · 2026Article
- Validation of a Multimodal Wearable Device for Assessing Environmental and Behavioral Risk Factors of Myopia in Children and Adolescents.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Systematic detection of predictive gene sets by semantics-based selection.BMC bioinformatics · 2026Article
- Senescence-Related SOCS1, SOCS2, and GADD45G Identify an Immune-Associated Molecular Signature in Metabolic Dysfunction-Associated Steatotic Liver Disease.Biomolecules · 2026Article
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
471 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSupervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.
methodsIn this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.
resultsWe found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.
conclusionThis study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.