ArticlePloS one2024
Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Development and internal validation of an interpretable machine learning prognostic prediction model for postoperative delirium after esophagectomy.Journal of thoracic disease · 2026Article
- Machine Learning for Valproic Acid Therapy: A Scoping Review of Pharmacokinetic-Prediction Models and Pharmacokinetic-Informed Clinical Outcome Models.Pharmaceutics · 2026Review
- Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review.Communications medicine · 2026Article
- A Machine Learning Framework for Prognostic Modeling in Stage III Colon Cancer.Journal of clinical medicine · 2026Article
- Development and validation of a novel blood-based biomarker for gastric cancer triage in chronic dyspepsia.NPJ digital medicine · 2026Article
- Visual interpretation of [European journal of nuclear medicine and molecular imaging · 2026Article
- The impact of a decision tree-based BOPPPS blended teaching model on cognitive performance in clinical epidemiology.Frontiers in public health · 2026Observational
- Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Chest X-Ray as a Screening Tool for Aortic Arch Dilation: CT-Based Evaluation of Reliability.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain.Scientific reports · 2025Article
- Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.Scientific reports · 2025Article
- Predicting car accident severity in Northwest Ethiopia: a machine learning approach leveraging driver, environmental, and road conditions.Scientific reports · 2025Article
- A Machine Learning Approach for Predicting the Pure-Component Surface Tension of Atmospherically Relevant Organic Compounds.ACS ES&T air · 2025Article
- Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach.Translational psychiatry · 2025Article
- Patterns of childhood trauma co-occurrence and its predictivity for suicidality: A machine learning approach.iScience · 2025Article
- Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.PloS one · 2025Article
- Minimizing unnecessary tax audits using multi-objective hyperparameter tuning of XGBoost with focal loss.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p<0.001 significance level. This performance superiority is consistent across both the model development and test phases. This study also used paired-sample t-tests for the subsets of the research datasets from disease prediction (66) and university-ranking (50) research contexts for further validation. The observed superiority of the tree-based ML algorithms remains valid for these subsets. Tree-based ML algorithms significantly outperformed non-tree-based algorithms for these two research contexts for all four performance measures. We discuss the research implications of these findings in detail in this article.
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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.