Machine Learning Model For Diabetes Prediction Using SVM And Random Forest
Author(s):
Gaddampally Saisri, Gaddaguti Pooja, Indhumathi S
Keywords:
Diabetes ,SVM, Random Forest, Blood Glucose.
Abstract
Diabetes is a chronic metabolic disorder affecting millions worldwide, with early detection being crucial for effective management and prevention of complications. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various clinical and demographic features. In this study, we present a comparative analysis of popular machine learning algorithms, Support Vector Machine (SVM), knn and Random Forest, for diabetes prediction. The dataset used in this study comprises a diverse range of demographic and clinical variables collected from a cohort of individuals, including age, gender, body mass index (BMI), family history of diabetes, blood pressure, and glucose level. At last, the aim is to predict diabetes in early stages and the one with good accuracy taken as the model for predicting the diabetes.
Article Details
Unique Paper ID: 162551

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 495 - 499
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