Enhancing Diabetes Detection Accuracy using an Ensemble Model of Random Forest and SVM

Padam Kshatriya1 Dr. Pankaj Richhariya2
  • 1Research Scholar, Department of Computer Science, BITS, Bhopal
  • 2Head of Department, Department of Computer Science, BITS, Bhopal

Keywords: Diabetes detection, Machine learning, Early diagnosis, Predictive analytics

Abstract

Diabetes is a prevalent chronic disease with significant health implications worldwide. Early detection plays a crucial role in effective management and prevention of complications. This research presents a comprehensive study on the application of machine learning techniques for the early detection of diabetes. The study compares multiple machine learning algorithms, explores data preprocessing techniques, and proposes an ensemble model to enhance accuracy and reliability. This research paper contributes to the field of early diabetes detection by developing an accurate and reliable machine learning model. The proposed ensemble model, combining SVM and Random Forest Classifier, surpasses individual algorithms in terms of accuracy. The research paper provides valuable insights for identifying individuals at risk of diabetes, facilitating timely interventions and improved healthcare outcomes.

PUBLISHED
2023-07-15
ISSUE
Vol 2 Issue 3 (2023)

Journal Details

   ISSN : 2583 – 7117

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