Metabolic Syndrome Management: Strategies for Early Detections and Preventive Interventions / (Record no. 614597)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02029nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Rehman, Sanam |
| 245 ## - TITLE STATEMENT | |
| Title | Metabolic Syndrome Management: Strategies for Early Detections and Preventive Interventions / |
| Statement of responsibility, etc. | Sanam Rehman |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 67p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | The prediction and management of metabolic syndrome (MetS) is crucial due to its chronic<br/>nature and global health challenge. This study aims for early and accurate MetS diagnosis<br/>for timely prevention by managing associated risk factors. It utilizes machine learning<br/>(ML) and deep learning (DL) techniques while considering demographic and ethnic<br/>variability. Notably, there is a lack of MetS prediction research in the Pakistani population,<br/>which has unique genetic and lifestyle diversity. This study addresses this gap using a<br/>dataset of 502 individuals from five Pakistani cities (MetS prevalence = 43.4%), with 24<br/>features from anthropometric, clinical, lifestyle, and family history data. It is the first study<br/>evaluating fifteen classifiers (12 ML and 3 DL models) through five-fold cross-validation.<br/>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<br/>glucose, systolic blood pressure, triglycerides, and obesity as key biomarkers for MetS.<br/>Odds ratio analysis across gender and age groups (95% CI) showed that Body Mass Index<br/>(BMI), blood pressure, and glucose levels were strongly associated with MetS in aging<br/>males, while glucose and HDL were more influential in older females. This study provides<br/>population-specific insights into MetS risk, enhancing early prediction accuracy and<br/>enabling targeted interventions for high-risk individuals. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Ahmed Fuwad |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/54549">http://10.250.8.41:8080/xmlui/handle/123456789/54549</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 08/29/2025 | 610 | SMME-TH-1151 | Thesis |