Design and Implementation of Low- cost Diabetes Level Prediction using Machine Learning

Diabetes is a chronic disease that affects millions of people worldwide. Machine learning techniques have been used to predict diabetes with high accuracy using clinical and lifestyle data. In this study, we propose a low-cost diabetes level prediction system using machine learning. The system will use a dataset of clinical and lifestyle data to train and test machine learning algorithms, including decision tree, random forest, and neural network. The proposed system aims to achieve high accuracy in diabetes prediction while minimizing the cost of implementation. The system will be implemented as a smart web application that can be accessed by healthcare professionals and patients. The results of this study will contribute to the development of low-cost and accessible diabetes prediction systems that can improve the quality of life of patients with diabetes. (Abstract)