Diabetes Prediction System Using SVM Alogrithm

Abstract

Diabetes Mellitus is a metabolic disease caused by high blood sugar, which can lead to serious health problems if not properly controlled. Early prediction and timely intervention are crucial for preventing and managing diabetes. This paper presents a Diabetic Prediction System utilizing the Support Vector Machine (SVM) algorithm, a powerful machine learning technique known for its effectiveness in classification tasks. The proposed system lever- ages a dataset comprising relevant features such as age, body mass index (BMI), family history, and blood pressure to train the SVM model. Data were preprocessed to control for missing values, normalize features, and reduce bias. The SVM algorithm is employed for classification, as it excels in handling high-dimensional data and is capable of finding optimal hyperplanes to separate different classes. The system undergoes a comprehensive evaluation using performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The results demonstrate the efficacy of the SVM algorithm in accurately predicting the likelihood of diabetes based on the input features.