• K.V.S.S.Ramakrishna
• K Baranitharan
• Ala Navya Sri
• Mullamananuri. Pallavi
• M.Jyoshna Kalyani, Y.Keerthi

Machine Learning (ML) techniques have shown significant promise in solving complex problems within dynamic environments, particularly in the study and management of diseases like diabetes. This research focuses on developing a personal healthcare system that uses data from daily activities and other relevant parameters to monitor blood glucose levels and provide online reports. By applying ML, especially the Random Forest algorithm, this system aims to detect non-communicable diseases such as diabetes mellitus at an early stage. The study reviews existing literature to understand previous methods used for disease detection. While various methods exist for non-invasive blood glucose measurement, a universally accurate and precise method has yet to be established. This paper emphasizes the effectiveness of Random Forest in predicting the likelihood of diabetes mellitus, thereby enabling early precautions.

Diabetes Prediction Using Random Forest Algorithm

"Diabetes Prediction Using Random Forest Algorithm ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.166-175, November-2024, Available :http://www.jetir.org/papers/JETIRGO06016.pdf

"Diabetes Prediction Using Random Forest Algorithm ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp166-175, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06016.pdf