ReviewDiagnostics (Basel, Switzerland)2022
Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ferritin and transferrin predict common carotid intima-media thickness in females: a machine-learning informed individual participant data meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Machine Learning-Based Risk Stratification Tool for Hearing Loss in High-Risk Neonates.The Laryngoscope · 2026Article
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Dental age estimation by comparing Demirjian's method and machine learning in Southeast Brazilian youth.Forensic science, medicine, and pathology · 2026Article
- Exploratory machine learning analysis to characterize angioscopic features associated with atherosclerosis-related aortic dissection: an exploratory single-center angioscopic study.Frontiers in cardiovascular medicine · 2026Article
- Medical statistics education in the big data era: focusing on critical thinking and ethical competence.Frontiers in public health · 2026Article
- Age estimation of children and adolescents from mandibles using machine learning.Scientific reports · 2025Article
- Predictive model of ibuprofen treatment failure in very preterm infants with patent ductus arteriosus using machine learning techniques.Journal of perinatology : official journal of the California Perinatal Association · 2025Article
- Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand.BMC public health · 2025Article
- An analysis of factors influencing dropout in methadone maintenance treatment program in Dehong Prefecture of China based on Cox regression and decision tree modelling.BMC health services research · 2025Article
- The relationship between medical students' interest in learning and their ability to solve mathematical problems: the chain-mediating role of teacher-student relationship and self-efficacy.Frontiers in psychology · 2025Article
- Pilot study: Initial investigation suggests differences in EMT-associated gene expression in breast tumor regions.Computational and structural biotechnology journal · 2025Article
- Advanced statistical methods for hazard modeling in cardiothoracic surgery: a comprehensive review of techniques and approaches.Indian journal of thoracic and cardiovascular surgery · 2024Article
- Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke.Frontiers in neurology · 2024Article
- Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors.PloS one · 2023Article
- Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics.Advances in ophthalmology practice and researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.
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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.