Predictive Analysis of Metabolic Syndrome Risk Factors

Project Overview

This project aims to identify key risk factors associated with Metabolic Syndrome (MetS) using predictive analytics. Metabolic Syndrome is a cluster of conditions—including hypertension, obesity, dyslipidemia, and insulin resistance—that increase the risk of cardiovascular disease and diabetes. By leveraging Python, statistical techniques, and machine learning models, this study explores significant predictors of MetS and provides insights for early detection and prevention.

Objectives

• Analyze the relationship between metabolic syndrome risk factors such as BMI, blood pressure, triglycerides, and fasting glucose.
• Develop predictive models to classify individuals at risk of MetS.
• Evaluate model performance using accuracy, precision, recall, and AUC-ROC.
• Visualize patterns and correlations using data visualization techniques.

Technologies & Tools Used

Programming Language: Python

Libraries: Pandas, NumPy, Matplotlib, Scipy, Statsmodel

Statistical Methods: One-way ANOVA, Two-way ANOVA, Linear Regression, Correlation Analysis

Data Processing: Feature scaling, outlier detection, and missing value imputation

Key Findings

• BMI, triglycerides, and fasting glucose levels were the strongest predictors of MetS.
• Machine learning models, particularly Random Forest and Logistic Regression, demonstrated high predictive accuracy.
• The model outputs suggest that early lifestyle interventions can reduce the risk of developing MetS.

Conclusion

This project provides valuable insights into the early detection of Metabolic Syndrome using data-driven approaches. The findings can help healthcare professionals make informed decisions and develop targeted intervention strategies for at-risk individuals.