Machine Learning Models for Early Detection of Diabetes in High-Risk Individuals

EasyChair Preprint 13738

Abstract

Diabetes is a chronic metabolic disorder that affects millions of people worldwide and poses significant health risks if left undetected or uncontrolled. Early detection of diabetes in high-risk individuals is crucial for effective intervention and management. Machine learning models have emerged as valuable tools for identifying patterns and predicting disease outcomes based on complex datasets. In this abstract, we present an overview of machine learning models for early detection of diabetes in high-risk individuals. We discuss the importance of early detection, the identification of risk factors, data collection, preprocessing, feature engineering, and the selection of appropriate machine learning algorithms. We highlight the significance of model training, validation, optimization, and interpretability in the context of diabetes detection. Furthermore, we explore the deployment and integration of these models into healthcare systems, emphasizing privacy and security considerations. Finally, we discuss the evaluation and performance metrics for assessing the effectiveness of early detection and compare the results with existing diagnostic methods. The findings underscore the potential of machine learning models in improving the early detection of diabetes, thereby enabling timely interventions and enhanced patient outcomes.

Keyphrases: Bayesian optimization, Grid Search, Hyperparameter Tuning, Random Search, evaluation metric, model optimization

@booklet{EasyChair:13738,
  author    = {Elizabeth Henry},
  title     = {Machine Learning Models for Early Detection of Diabetes in High-Risk Individuals},
  howpublished = {EasyChair Preprint 13738},
  year      = {EasyChair, 2024}}