Obesity Detection

Obesity Detection

Author Name : Akash Mhetre, Preet Pochat, .Mitali Bora, Aniket Dhane, Niranjan Desai

ABSTRACT : Obesity is a critical health issue globally, demanding innovative solutions for detection and intervention. This paper presents an implementation framework leveraging machine learning (ML) techniques, including Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and Logistic Regression (LR), in Python. The objective is to develop a robust system for obesity detection, aiding in early diagnosis and personalized interventions. The integration of ML algorithms offers a data-driven approach, allowing for accurate prediction and classification of obesity risk factors. Through Python implementation, we demonstrate the efficacy of SVM, RF, DT, and LR in analyzing diverse datasets to identify patterns indicative of obesity. Our approach emphasizes scalability, interpretability, and performance optimization, enabling seamless integration into existing healthcare systems. By harnessing ML algorithms, this research contributes to advancing obesity detection methodologies, fostering proactive healthcare interventions, and ultimately improving public health outcomes.