000			02029nam a22001577a 4500
082			_a610
100			_aRehman, Sanam _9130436
245			_aMetabolic Syndrome Management: Strategies for Early Detections and Preventive Interventions / _cSanam Rehman
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a67p. _bSoft Copy _c30cm
500			_aThe prediction and management of metabolic syndrome (MetS) is crucial due to its chronic nature and global health challenge. This study aims for early and accurate MetS diagnosis for timely prevention by managing associated risk factors. It utilizes machine learning (ML) and deep learning (DL) techniques while considering demographic and ethnic variability. Notably, there is a lack of MetS prediction research in the Pakistani population, which has unique genetic and lifestyle diversity. This study addresses this gap using a dataset of 502 individuals from five Pakistani cities (MetS prevalence = 43.4%), with 24 features from anthropometric, clinical, lifestyle, and family history data. It is the first study evaluating fifteen classifiers (12 ML and 3 DL models) through five-fold cross-validation. AdaBoost outperformed with 93.4% accuracy, an Area under Curve (AUC) of 0.97, and pvalue < 0.05. Feature importance analysis (Permutation and SHAP) identified fasting blood glucose, systolic blood pressure, triglycerides, and obesity as key biomarkers for MetS. Odds ratio analysis across gender and age groups (95% CI) showed that Body Mass Index (BMI), blood pressure, and glucose levels were strongly associated with MetS in aging males, while glucose and HDL were more influential in older females. This study provides population-specific insights into MetS risk, enhancing early prediction accuracy and enabling targeted interventions for high-risk individuals.
650			_aMS Biomedical Sciences (BMS)
700			_aSupervisor : Dr. Ahmed Fuwad _9123326
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/54549
942			_2ddc _cTHE
999			_c614597 _d614597