• K. Harini
• K.S.Hemalatha
• D.Kavipriya
• A.R.Nalini
• C.Visali

The remarkable advancements in public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the first onset detection and prevention of several fatal diseases. Diabetes mellitus is a particularly life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. During this paper, a machine learning-based approach has been proposed for the classification, early-stage identification, and prediction of diabetes.

KEYWORDS: Diabetes mellitus, Algorithms used are Logistic Regression (LR), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Random Forest (RF), XG Boost, Gradient Boost.

"DIABETES PREDICTION IN HEALTHCARE SYSTEMS USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.k246-k255, May-2022, Available :http://www.jetir.org/papers/JETIR2205B31.pdf

"DIABETES PREDICTION IN HEALTHCARE SYSTEMS USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppk246-k255, May-2022, Available at : http://www.jetir.org/papers/JETIR2205B31.pdf