Longitudinal Trends in Noncommunicable Disease Risk Factors and Premature Mortality in Saudi Arabia: A 33-Year Ecological Time-Series Study with Machine Learning Prediction
Issued Date
2026-06-01
Resource Type
eISSN
20770383
Scopus ID
2-s2.0-105041632205
Journal Title
Journal of Clinical Medicine
Volume
15
Issue
11
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Clinical Medicine Vol.15 No.11 (2026)
Suggested Citation
Alnomasy N., Banappagoudar S.B., Alrashedi H., Mahmoud S.K.M., Abouhashish E., Ruksakulpiwat S. Longitudinal Trends in Noncommunicable Disease Risk Factors and Premature Mortality in Saudi Arabia: A 33-Year Ecological Time-Series Study with Machine Learning Prediction. Journal of Clinical Medicine Vol.15 No.11 (2026). doi:10.3390/jcm15114387 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117429
Title
Longitudinal Trends in Noncommunicable Disease Risk Factors and Premature Mortality in Saudi Arabia: A 33-Year Ecological Time-Series Study with Machine Learning Prediction
Corresponding Author(s)
Other Contributor(s)
Abstract
Background/Objectives: In Saudi Arabia, noncommunicable diseases (NCDs) are an increasing public health concern, with almost 70% of deaths related to chronic diseases. The study aimed to analyze 33-year trends in NCD risk factors and apply machine learning (ML) models to identify ecological associates of premature NCD-related mortality, sex-specific analyses and project trajectories to 2030. Methods: A longitudinal ecological time-series design which used WHO Global Health Observatory (GHO) NCD Indicators (1990–2022; select lipid indicators from 1980). Five supervised regression ML models—OLS, LASSO, Ridge, Random Forest, and Gradient Boosting—were trained with TimeSeriesSplit cross-validation (five folds) to preserve temporal order and prevent data leakage. A formal PELT changepoint algorithm confirmed trend breakpoints. Linear projections to 2030 were estimated with 95% prediction intervals. Results: Adult obesity increased by +20.6 percentage points (pp) over 33 years. Under a no-policy-change scenario, female obesity is projected at 50.3% by 2030 (95% PI: 50.0–50.5%). Premature NCD mortality declined by −5.9 pp. Under TimeSeriesSplit CV, all models yielded negative R<sup>2</sup>, confirming LOOCV R<sup>2</sup> = 0.98 reflected shared time-trend artefacts; the ML component is reframed as descriptive feature-importance analysis. The obesity sex gap (female minus male) was the strongest ecological associate of premature NCD mortality. Diabetes treatment coverage showed a strong inverse ecological association (r = −0.913). Conclusions: NCD risk factors in Saudi Arabia are evolving in complex ways. Targeted interventions addressing sex-specific disparities and healthcare system performance are urgently needed to meet national and global NCD targets.