Diabetes Prediction Using Machine Learning

Diabetes, a pervasive global health concern, stems from elevated blood glucose levels and manifests through symptoms like frequent urination, increased thirst, and heightened hunger. Its widespread impact includes severe complications such as heart failure, stroke, kidney failure, amputation, and blindness. In response to this critical challenge, the evolving realm of data science, particularly machine learning, has emerged as a dynamic force. This discipline, rooted in machines’ experiential learning, aims to pioneer an advanced predictive system for early diabetes detection. Pioneering research in this field amalgamates diverse machine learning methodologies, including K-nearest neighbor, decision tree, random forest, and support vector machine. A primary dataset of high-risk, moderate-risk, and low-risk diabetes is used for the prediction purpose in this work. The support vector machine model has shown better performance in the classification of diabetic patients. The quest for the most reliable algorithm drives the creation of a robust diabetes prediction model. This model stands poised to revolutionize healthcare, marking a proactive leap toward innovation and early intervention in diabetes management. Such advancements hold the potential to transform healthcare practices and significantly impact global health outcomes for individuals affected by this chronic condition.