Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies.
Despite efforts by the World Health Organization (WHO), tuberculosis (TB) remains a leading cause of fatalities globally. This study reviews time series and machine learning models for TB incidence prediction, identifies popular algorithms, and highlights the need for further research to improve acc...
| Publicado en: | International Journal of Environmental Health Research Vol. 35; no. 3; pp. 645 - 661 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183372373&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183372373 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Mar2025 vid: 35 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 183372373 178061074 183372373 183372373 10.1080/09603123.2024.2368137 183372373 ppf: 645 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies. aug: au: Maipan-Uku, Jamilu Yahaya Cavus, Nadire affil: Department of Computer Science, Ibrahim Badamasi Babangida University, Lapai, Nigeria sug: subj: Tuberculosis Epidemiology Time Series Machine Learning Algorithms Prediction Models Human Systematic Review PubMed Gray Literature Prediction Algorithms Tuberculosis Diagnosis Tuberculosis Prevention and Control Geographic Factors Quality Assessment Descriptive Statistics ab: Despite efforts by the World Health Organization (WHO), tuberculosis (TB) remains a leading cause of fatalities globally. This study reviews time series and machine learning models for TB incidence prediction, identifies popular algorithms, and highlights the need for further research to improve accuracy and global scope. SCOPUS, PUBMED, IEEE, Web of Science, and PRISMA were used for search and article selection from 2012 to 2023. The results revealed that ARIMA, SARIMA, ETS, GRNN, BPNN, NARNN, NNAR, and RNN are popular time series and ML algorithms adopted for TB incidence rate predictions. The inaccurate TB incidence prediction and limited global scope of prior studies suggest a need for further research. This review serves as a roadmap for the WHO to focus on regions that require more attention for TB prevention and the need for more sophisticated models for TB incidence predictions. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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