Analysis on Symptoms Driven Disease Risk Assessment using Artificial Intelligence Approach

An important part of healthcare is disease risk assessment, which aids in early diagnosis, prevention, and personalized treatment plans. Symptoms, as major indications of underlying health issues, provide critical information for appropriate risk assessment. This review paper investigates the junction of Symptom Driven Disease Risk Assessment using Artificial Intelligence, providing an in-depth examination of current advances and problems in this field. Through this holistic approach to disease prediction and personalized health recommendations, the AI-driven system aims to revolutionize healthcare by empowering individuals to proactively manage their health, leading to a healthier society and ultimately reducing the strain on healthcare systems. The intricacies of symptom analysis and disease prediction were discovered by comparing different approaches of machine learning as well deep learning models across several trials. The findings of the comparison reveal intriguing patterns and inequalities, offering light on the changing environment of symptom-driven risk assessment that can pave the way for refined methodologies and robust models that can better serve the goal. Comparison and contrast of numerous studies, methodologies, challenges, and outcomes through an extensive literature review was done to provide an extensive review of the current state of research on this topic.