ArticleIranian journal of public health2012
Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.
Article in Iranian journal of public health, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Integrating multidimensional data analytics for precision diagnosis of chronic low back pain.Scientific reports · 2025Article
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Determinants of Inadequate Empiric Antimicrobial Therapy in ICU Sepsis Patients in Al-Madinah Al-Munawwarah, Saudi Arabia: A Comparison of Artificial Neural Network and Regression Analysis.Antibiotics (Basel, Switzerland) · 2023Article
- Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network.Frontiers in oncology · 2023Article
- Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice.Cureus · 2022Review
- Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.BMC medical research methodology · 2020Article
- 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
- A Review on the Use of Artificial Intelligence in Spinal Diseases.Asian spine journal · 2020Article
- Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2020Article
- Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.NPJ digital medicine · 2020Review
- Development and validation of a pre-scoring system for nonspecific low back pain among general population in Guangzhou: a cross-sectional study.BMC public health · 2019Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain.
methodsData from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria.
resultsThe area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively.
conclusionsBased on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.
Indexed as
Identifiers
23113198PMC3469002What 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.