Developing a Naïve Bayes risk classification machine learning algorithm to predict high viral load in a low-resource setting.
| Published in: | PLoS Global Public Health Vol. 6; no. 5; pp. 1 - 10 |
|---|---|
| Main Authors: | , |
| Format: | research tables/charts Journal Article |
| Published: |
Public Library of Science
5/22/2026
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193981463&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193981463 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27673375 MP7X jtl: PLoS Global Public Health issn: 27673375 maglogo: N pubinfo: dt: 5/22/2026 vid: 6 iid: 5 pid: 16707 pub: Public Library of Science place: San Francisco, California artinfo: ui: 193981463 193981463 193981463 10.1371/journal.pgph.0006373 193981463 ppf: 1 ppct: 9 formats: tig: atl: Developing a Naïve Bayes risk classification machine learning algorithm to predict high viral load in a low-resource setting. aug: au: Gonah, Laston Murakwani, Trymore affil: School of Public Health, Faculty of Medicine and Health Sciences, Walter Sisulu University, Mthatha, South Africa sug: subj: Machine Learning Algorithms Utilization Prediction Models Resource-Limited Settings Probability Viral Load Evaluation HIV Infections Drug Therapy Anti-Retroviral Agents Therapeutic Use HIV Infections Risk Factors Risk Assessment Predictive Validity Evaluation Human Male Female Infant, Newborn Infant Child, Preschool Child Adolescence Adult Middle Age Retrospective Design Record Review Case Control Studies Secondary Analysis HIV-Positive Persons Psychosocial Factors Zimbabwe Sensitivity and Specificity Reproducibility of Results Age Factors Marital Status Functional Status Weight Loss Treatment Duration Health Screening Monitoring, Physiologic Univariate Statistics Bivariate Statistics Multivariate Analysis Multiple Logistic Regression Pregnancy Breast Feeding HIV Seropositivity Antibiotic Prophylaxis Descriptive Statistics Data Analysis Software Chi Square Test Odds Ratio Confidence Intervals Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|