Abstract

Tailoring long-term care for individuals with chronic conditions such as Type 2 Diabetes (T2D) is imperative due to the variability in responses observed among patients, even when undergoing identical treatments. Analyzing extensive patient data, often referred to as "big data," presents a promising avenue for studying the diverse manifestations and impact of T2D, utilizing the wealth of digitized patient records. The field of data science can significantly contribute to the customization of care plans, validation of established medical knowledge, and discovery of valuable insights within the extensive healthcare datasets. This comprehensive review introduces a framework for effectively managing T2D, covering various stages, including exploratory analysis, predictive modeling, and visual data exploration techniques. This integrated approach empowers healthcare professionals and researchers to identify meaningful correlations between a patient's diverse biological markers and the complications associated with T2D. By utilizing this framework, it becomes possible to predict how an individual will respond to specific treatments, categorize T2D patients into distinct profiles associated with particular conditions, and assess the likelihood of complications linked to T2D.

Description