ArticleJournal of medical systems2012
Comparison of artificial neural networks with logistic regression for detection of obesity.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- A survey on data mining techniques used in medicine.Journal of diabetes and metabolic disorders · 2021Article
- Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models.Journal of advanced veterinary and animal research · 2020Article
- Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.Journal of medical systems · 2016Article
- Hybrid EANN-EA System for the Primary Estimation of Cardiometabolic Risk.Journal of medical systems · 2016Article
- Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.PloS one · 2016Article
- A Comparison of Logistic Regression Model and Artificial Neural Networks in Predicting of Student's Academic Failure.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2015Article
- Prediction of trauma-specific death rates of pedestrians of Fars Province, Iran.Electronic physician · 2015Article
- Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.PloS one · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obesity is a common problem in nutrition, both in the developed and developing countries. The aim of this study was to classify obesity by artificial neural networks and logistic regression. This cross-sectional study comprised of 414 healthy military personnel in southern Iran. All subjects completed questionnaires on their socio-economic status and their anthropometric measures were measured by a trained nurse. Classification of obesity was done by artificial neural networks and logistic regression. The mean age±SD of participants was 34.4 ± 7.5 years. A total of 187 (45.2%) were obese. In regard to logistic regression and neural networks the respective values were 80.2% and 81.2% when correctly classified, 80.2 and 79.7 for sensitivity and 81.9 and 83.7 for specificity; while the area under Receiver-Operating Characteristic (ROC) curve were 0.888 and 0.884 and the Kappa statistic were 0.600 and 0.629 for logistic regression and neural networks model respectively. We conclude that the neural networks and logistic regression both were good classifier for obesity detection but they were not significantly different in classification.
Indexed as
Identifiers
21556898What 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.