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The Effect of Machine Learning Algorithms in the Prediction, and Diagnosis of Meningitis: A Systematic Review Publisher



Ghaddaripouri K1 ; Ghaddaripouri M2 ; Mousavi AS3 ; Mousavi Baigi SF4 ; Rezaei Sarsari M5 ; Dahmardeh Kemmak F4 ; Mazaheri Habibi MR6
Authors
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Authors Affiliations
  1. 1. Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
  2. 2. Department of Laboratory Sciences, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. Mashhad University of Medical Sciences, Mashhad, Iran
  4. 4. Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
  5. 5. Department of Health Information Technology, Tehran University of Medical Sciences, Tehran, Iran
  6. 6. Department of Health Information Technology, Varastegan Institute for Medical Sciences, Mashhad, Iran

Source: Health Science Reports Published:2024


Abstract

Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers. © 2024 The Authors. Health Science Reports published by Wiley Periodicals LLC.