Proactive Health Monitoring: Predictive Analytics for Early Detection of Diabetes Risk

EasyChair Preprint 15225

Abstract

This research introduces an advanced predictive analytics framework for the early detection of diabetes risk, aiming to enhance proactive health monitoring through the integration of sophisticated machine learning algorithms. The model is meticulously trained on a diverse set of patient health metrics, including demographic and clinical variables such as age, body mass index, blood pressure, and glucose levels. By identifying subtle patterns and correlations within the data, the model facilitates the early identification of individuals at high risk of developing diabetes. This early detection capability enables timely clinical interventions, potentially mitigating the progression of the disease and optimizing patient management strategies. The study underscores the model's robustness and scalability, highlighting its significant potential for deployment in clinical settings as a critical component of preventive healthcare infrastructure.

Keyphrases: Diabetes Risk Prediction, Early disease detection, Machine Learning in Healthcare, Predictive Analytics

@booklet{EasyChair:15225,
  author    = {Ashish Mahindre and Sarika Kondekar},
  title     = {Proactive Health Monitoring: Predictive Analytics for Early Detection of Diabetes Risk},
  howpublished = {EasyChair Preprint 15225},
  year      = {EasyChair, 2024}}