ReviewCureus2025
Support Vector Machines: A Literature Review on Their Application in Analyzing Mass Data for Public Health.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
- The power of machine learning models in predicting gestational diabetes mellitus.BMC pregnancy and childbirth · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
- Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- An Artificial Intelligence-Based Model to Predict Pregnancy After Intrauterine Insemination: A Retrospective Analysis of 9501 Cycles.Journal of personalized medicine · 2025Article
- Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest.Healthcare (Basel, Switzerland) · 2025Article
- A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis.Interdisciplinary perspectives on infectious diseases · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study considers the literature on support vector machines (SVMs) in the area of public health data analysis, particularly evaluating their ability to harness big data for disease classification and health predictions. SVMs have been remarkably embraced for two decades in clinical diagnosis, patient management, and prediction of health trends owing to their high precision and robustness. This review suggests the ability of the method to support spatially relevant health system responses through the assessment of SVM advantages and disadvantages in public health and future research agendas, including improving scalability, integrating SVMs with emerging data sources like the Internet of Things (IoT) and genomic data, and enhancing model transparency to support real-world public health decision-making.
Indexed as
Identifiers
What OpenQuestion holds
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.