DIABETES DISEASE PREDICTION USING MACHINE LEARNING ALGORITHMS

Abstract

Diabetes is one of the most prevalent chronic diseases worldwide, affecting millions of individuals and posing significant challenges to healthcare systems. Early detection and accurate prediction of diabetes are essential for preventing severe complications such as cardiovascular diseases, kidney failure, nerve damage, and vision impairment. Traditional diagnostic methods often require extensive clinical examinations and laboratory testing, which may delay timely intervention. This paper presents a diabetes disease prediction framework using machine learning algorithms to assist in the early identification of diabetic patients. The proposed system utilizes patient health parameters such as glucose levels, blood pressure, body mass index, insulin levels, age, and other clinical indicators to develop predictive models. Various machine learning algorithms are employed to analyze medical data, identify hidden patterns, and classify individuals as diabetic or non-diabetic. Data preprocessing, feature selection, and model optimization techniques are incorporated to enhance prediction accuracy and reliability. Experimental analysis demonstrates that machine learning-based prediction models effectively improve diagnostic performance, support early disease detection, and assist healthcare professionals in clinical decisionmaking. The proposed framework offers an intelligent, cost-effective, and scalable solution for diabetes risk assessment and healthcare management.