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An Interactive AI Approach for Disease Prediction System Using Symptom Analysis

Authors:
K. Lalitha, R. Vani, R.Ragesha, Rejwan Bin Sulaiman

Addresses:
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Science and Humanities, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science and Technology, Northumbria University, London, United Kingdom.

Abstract:

Most diseases start with a few main symptoms that are unique to them. Recognising these initial symptoms can preserve countless lives and improve the provision of healthcare. A new way to anticipate common diseases is discussed in this work. It is based on an interactive, user-friendly model that anyone may use. The rapid advancement of AI has opened up several avenues to enhance diagnostic processes in healthcare. This effort aims to improve the effectiveness of these initial medical tests, particularly in underserved areas. The analysis indicates that cardiovascular illnesses are still one of the top causes of death in the world, with 18.6 million fatalities in 2019, or roughly 36% of all deaths worldwide. Finding and treating symptoms like chest pain, shortness of breath, or fatigue early on greatly lowers the risk of bad outcomes linked to heart disease. An illness like tuberculosis, even though it isn't very common, can be diagnosed and treated early, which can raise the cure rate to 85–95%. The study demonstrates that the suggested model can predict diseases based on symptom input with 70.12% accuracy, establishing it as a valuable tool in medical diagnostics.

Keywords: Disease Prediction; Artificial Intelligence; Symptom Analysis; Machine Learning; Healthcare Accessibility; Medical Diagnostics; Medical Assessments; Patterns and Relationships.

Received on: 12/10/2024, Revised on: 21/12/2024, Accepted on: 08/02/2025, Published on: 05/06/2025

DOI: 10.69888/FTSHSL.2025.000462

FMDB Transactions on Sustainable Health Science Letters, 2025 Vol. 3 No. 2, Pages: 101-113

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