ArticleJournal of preventive medicine and hygiene2021
Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.
Article in Journal of preventive medicine and hygiene, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Prevalence of epsilon toxin-producing Clostridium perfringens isolates among patients with multiple sclerosis and neuromyelitis optica spectrum disorder in Iran.BMC neurology · 2025Article
- Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.Journal of clinical orthopaedics and trauma · 2024Article
- Assessing treatment switch among patients with multiple sclerosis: A machine learning approach.Exploratory research in clinical and social pharmacy · 2023Article
- Review
- Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity.International journal of environmental research and public health · 2023Article
- Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting MS patients.
methodsThe study used information regarding 200 patients through a case-control study conducted in Hamadan, Western Iran, from 2013 to 2015. The performance of six classifiers was used to compare their performance in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and total accuracy.
resultsRandom Forest (RF) model illustrated better performance among other models in both scenarios. It had greater specificity (0.67), PPV (0.68) and total accuracy (0.68). The most influential diagnostic factors for MS were age, birth season and gender.
conclusionsOur findings showed that despite all the six methods performed almost similarly, the RF model performed slightly better in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting MS in the early stage and control the disease.
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