Predicting Obesity Trends Using Machine Learning from Big Data Analytics Approach
Gopichand Vemulapalli, Sreedhar Yalamati, Naga Ramesh Palakurti, Naved Alam, Srinivas Samayamantri, Pawan Whig
Abstract
This research paper explores the application of machine learning techniques in predicting obesity trends through big data analytics (BDA). Obesity has become a global health concern with significant socio-economic implications. Traditional methods of studying and addressing obesity trends often lack the scalability and efficiency required to handle large volumes of diverse data sources. Leveraging machine learning algorithms and big data analytics offers a promising approach to understanding and predicting obesity prevalence Our study focuses on harnessing machine learning models to analyze extensive datasets encompassing demographic, socioeconomic, environmental, and lifestyle factors. Through the integration of various data sources, including electronic health records, wearable devices, and social media, our research aims to uncover hidden patterns and correlations contributing to obesity trends. By employing predictive analytics, our model seeks to forecast future obesity rates and identify high-risk populations, facilitating targeted interventions and policy implementations. The findings of this research contribute to the advancement of data-driven approaches in public health and offer valuable insights for policymakers, healthcare professionals, and researchers striving to combat the obesity epidemic. Embracing machine learning and big data analytics presents opportunities for more proactive and personalized interventions, ultimately fostering healthier communities worldwide.