Semantic Precision: Forecasting Healthy Practices for Diabetic Patients Using Social Media Perspective and Medical Models

Abstract

Hyperglycemia mellitus is a chronic health condition that affects millions of people globally, posing significant challenges to both individuals and healthcare systems. The condition arises due to the body's inability to produce enough insulin, as seen in Type 1 diabetes, or its inability to effectively utilize insulin, as in Type 2 diabetes [1]. Managing diabetes requires a multifaceted approach, including regular monitoring of blood sugar levels, adherence to medication regimens, maintaining a balanced diet, engaging in physical activity, and being vigilant for potential symptoms [2]. Poor management can lead to severe complications such as cardiovascular diseases, kidney damage, nerve damage, and vision impairment [3]. This thesis proposes a novel framework to simplify and enhance diabetes care by leveraging real world data, process modeling, and advanced machine learning techniques.