Efficient Diabetes Detection And Diet Recommendation System Using Ensemble Framework On Big Data Clouds

Diabetes is a growing global health concern, requiring early detection and effective management strategies. In the era of big data, leveraging healthcare data for timely diagnosis and personalized diet recommendations can significantly improve patient outcomes. It proposes an integrated approach for detecting diabetes and recommending personalized diet plans using an ensemble framework within healthcare big data clouds. It combines various machine learning models to improve the accuracy of diabetes prediction by analyzing patient data, such as medical history, lifestyle factors, and biometric readings, stored in cloud-based environments. The ensemble approach integrates multiple models, such as Decision tree, Support Vector Machine, and Artificial neural networks(ANN), to enhance prediction performance. Once a diabetes diagnosis is confirmed, it generates personalized diet plans based on the patient’s health conditions, dietary preferences, and nutritional requirements. By utilizing cloud infrastructure, the framework ensures scalability and efficiency in processing large volumes of patient data, making it suitable for real-time deployment in healthcare systems. It not only improves the accuracy of diabetes detection but also provides a tailored dietary plan, thus offering a comprehensive solution for diabetes management. It demonstrates that integrating machine learning with cloud-based data storage and analysis can provide significant advantages in early diagnosis, personalized care, and overall health outcomes in diabetic patients. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on comprehensive investigation it classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence.