A dynamic hybrid model for efficient tuberculosis incidence rate prediction.
Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for...
| Publicado en: | International Journal of Environmental Health Research Vol. 35; no. 3; pp. 541 - 555 |
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| Autores principales: | , |
| Formato: | equations & formulas research 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=183372365&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183372365 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: 183372365 177644713 183372365 183372365 10.1080/09603123.2024.2362833 183372365 ppf: 541 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A dynamic hybrid model for efficient tuberculosis incidence rate prediction. aug: au: Maipan-Uku, Jamilu Yahaya Cavus, Nadire affil: Department of Computer Science, Ibrahim Badamasi Babangida University, Lapai, Nigeria sug: subj: Tuberculosis Risk Factors Prediction Models Risk Assessment Tuberculosis Prevention and Control Forecasting (Research) Human Health and Welfare Planning Tuberculosis Therapy Descriptive Statistics Predictive Validity Policy Making Health Personnel Education World Health Comparative Studies ab: Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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