Diabetes is a metabolic disorder that results from deficiency of the insulin secretion to control high sugar contents in the body system. At early stage, diabetes can be managed and controlled. Prolong diabetes leads to complication disorders such as diabetes retinopathy, angina, heart attack, stroke, atherosclerosis and even death. Therefore, assessment of diabetic risk prediction is necessary at early stage by using machine learning classification techniques based on observed sample features. The dataset used for this paper was obtained from Irvine (UCI) repository of machine learning databases and was analyzed on WEKA application platform. The dataset contains 520 samples with 17 distinct attributes. Machine learning algorithms used as classifier are K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM),

Keywords : Diabetes is a metabolic disorder that results from deficiency of the insulin secretion to control high sugar contents in the body system. At early stage, diabetes can be managed and controlled. Prolong diabetes leads to complication disorders such as diabetes retinopathy, angina, heart attack, stroke, atherosclerosis and even death. Therefore, assessment of diabetic risk prediction is necessary at early stage by using machine learning classification techniques based on observed sample features. The dataset used for this paper was obtained from Irvine (UCI) repository of machine learning databases and was analyzed on WEKA application platform. The dataset contains 520 samples with 17 distinct attributes. Machine learning algorithms used as classifier are K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM),