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A Comprehensive Evaluation of Diabetes Prediction Algorithms Using Microsoft Azure Machine Learning Studio

  • Unique Paper ID: 167581
  • Volume: 11
  • Issue: 3
  • PageNo: 1634-1639
  • Abstract:
  • The early prediction of diabetes is crucial for effective disease management and prevention. With the advent of machine learning (ML) technologies, the ability to predict diabetes has significantly improved, leveraging diverse algorithms to identify patterns and correlations in medical data. This study presents a comprehensive performance analysis of various classification algorithms utilized for diabetes prediction using Microsoft Azure Machine Learning Studio. We explore and compare the efficacy of algorithms such as Decision Trees, Logistic Regression, Support Vector Machines (SVM), and Neural Networks in predicting diabetes based on a set of clinical variables. The performance of these models is evaluated using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Our findings highlight the strengths and limitations of each algorithm, providing insights into the most effective approaches for diabetes prediction. This paper contributes to the ongoing efforts to enhance predictive analytics in healthcare, offering practical guidance for selecting appropriate machine learning techniques in similar medical applications.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 1634-1639

A Comprehensive Evaluation of Diabetes Prediction Algorithms Using Microsoft Azure Machine Learning Studio

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