Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 18, 2025Open Access

A Machine Learning Approach to Prediction and Multimorbidity Risk Factor Identification in a low- and middle-income country

View Full Paper
Ask AI
Bookmark
Share

Authors

OUOlalekan A. UthmanMHMatthew HazellMUMuhammed Mubashir B Uthman

Discussion

Loading...

Member takes

Overview

Cross-sectional analysis identifies key predictors of multimorbidity in individuals, indicating the importance of contextual factors.

Key Points

  • The Gradient Boosting Classifier achieved an AUC of 0.7809 for predicting multimorbidity.
  • Age, medication use, sex, and community illiteracy rate were identified as influential predictors.
  • This cross-sectional study utilized data from the South Africa Demographic and Health Survey 2016 with 5,342 participants.
  • Findings underscore the need for targeted intervention strategies addressing both individual-level and contextual factors.

Cite This Study

Uthman et al. (2025) studied this question.

synapsesocial.com/papers/68f408995de60f8893c6fe5dhttps://doi.org/10.1101/2025.10.13.25337900
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey2025
  2. 2Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions2025 · 6 citations
  3. 3Machine learning-based early prediction of multiple chronic disease risk in aging Chinese population: A longitudinal analysis using CHARLS data2026
  4. 4Machine Learning Classification of Prevalent Chronic Disease Using Multidimensional Social, Behavioral, and Psychological Determinants: A Cross-Sectional Analysis of the 2024 National Health Interview Survey2026
  5. 5Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction2025