Hybrid Methodology for Predicting Hypertension in Patients with Dyslipidemia and Type 2 Diabetes Mellitus

This study uses statistical computational methods to model hypertension in patients with dyslipidemia and type 2 diabetes. A retrospective analysis of 39 patients from Hospital Universiti Sains Malaysia identified key factors like blood pressure, glucose, and cholesterol. A hybrid model combining bootstrap, logistic regression, and neural networks achieved 99.99% accuracy with a MAD of 0.0001. Eight factors were significantly associated with hypertension, demonstrating the model’s high predictive power and reliability for risk assessment.