A Review of Machine Learning Models for Metabolic Syndrome Prediction with Emphasis on Hybrid Ensemble Techniques

In recent years, machine learning has been widely applied in the healthcare field for disease prediction and risk analysis. Metabolic Syndrome (MetS) is a major health condition caused by multiple risk factors such as obesity, high blood pressure, abnormal lipid levels, and increased blood glucose. It is also closely associated with hyperuricemia, which can further increase long-term health risks. This paper presents a review of various machine learning techniques used for predicting metabolic syndrome. Different algorithms such as Logistic Regression, Random Forest, Support Vector Machine, XGBoost, and deep learning models have been explored to improve prediction performance. While some studies focus on individual models, others use ensemble and hybrid approaches to achieve better accuracy. The study also highlights that hyperuricemia is often treated as an associated risk factor rather than a direct prediction variable. Overall, ensemble and stacking models show better performance in handling complex clinical data. This work provides insights into existing methodologies, identifies research gaps, and suggests future directions for developing more reliable and interpretable healthcare prediction systems.