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Role of Machine Learning in Early Prediction of Diabetes Onset

EasyChair Preprint no. 13566

23 pagesDate: June 6, 2024


The early prediction of diabetes onset plays a crucial role in effective disease management and prevention of complications. Machine learning has emerged as a promising tool in healthcare, offering the potential to improve early prediction accuracy and enable personalized interventions. This abstract discusses the role of machine learning in early prediction of diabetes onset.


The abstract covers various aspects, starting with an introduction to diabetes and the importance of early prediction. It then provides an overview of machine learning and its potential in healthcare. The abstract delves into the challenges associated with early prediction of diabetes and highlights how machine learning can address these challenges.


The abstract outlines the specific roles of machine learning in early prediction, including data collection and preprocessing, feature selection and extraction, model development and training, validation and evaluation, and risk stratification. It emphasizes the significance of personalized interventions based on individuals' risk profiles.

Keyphrases: Complex decision, Cost and accessibility, Ethical and privacy concerns, Generalizability, Integration into clinical practice, Limited evidence and validation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Edwin Frank},
  title = {Role of Machine Learning in Early Prediction of Diabetes Onset},
  howpublished = {EasyChair Preprint no. 13566},

  year = {EasyChair, 2024}}
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