Leveraging Machine Learning Algorithms for Predicting Diabetes Onset in At-Risk Populations

EasyChair Preprint 13740

Abstract

Diabetes is a prevalent chronic disease that poses significant health risks to individuals and burdens healthcare systems worldwide. Early detection and prediction of diabetes onset in at-risk populations play a crucial role in implementing preventive measures and improving health outcomes. Leveraging machine learning algorithms offers promising opportunities for accurate and efficient prediction models. This paper presents an overview of the application of machine learning algorithms for predicting diabetes onset in at-risk populations. The study discusses data collection and preprocessing techniques, feature selection, and engineering methods to extract informative features. Various supervised and unsupervised machine learning algorithms are explored, along with model training, evaluation, and optimization strategies. Additionally, interpretability and explainability techniques are discussed to enhance model transparency. The deployment and real-world application of the developed models are highlighted, considering scalability, performance, and ethical considerations. The limitations and challenges of utilizing machine learning algorithms in this context are also addressed. Overall, leveraging machine learning algorithms for predicting diabetes onset in at-risk populations holds great potential for early intervention and improved public health outcomes. Further research and advancements in this field can lead to more accurate and personalized prediction models, ultimately aiding in effective preventive strategies and healthcare resource allocation.

Keyphrases: Data Quality, Deployment, Diabetes Prediction, Explainability, interpretability, machine learning, real-world application

@booklet{EasyChair:13740,
  author    = {Elizabeth Henry},
  title     = {Leveraging Machine Learning Algorithms for Predicting Diabetes Onset in At-Risk Populations},
  howpublished = {EasyChair Preprint 13740},
  year      = {EasyChair, 2024}}