Early Stage Disease Identification in a Context-aware Environment Leveraging Digital Security and Natural Language Processing

Abstract

Using natural language processing and machine learning, an innovative system for the diagnosis of early-stage diseases is developed within a context-aware environment. The system combines extracted symptoms with contextual data, such as age, gender, and location, to enhance the accuracy of diagnosis. For symptom-specific studies, using a reinforcement learning framework ensures better results in cases of uncertain or insufficient input. By putting together patient demographics and researching symptom development, this method will provide a more specific and precise diagnosis. The outcomes of health care should therefore be efficiently improved through this sophisticated AI-driven approach