Publication Details

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into healthcare has revolutionized diagnostic processes, treatment planning, and patient management. Early disease prediction using ML models has gained significant attention due to its potential to reduce mortality rates, improve treatment outcomes, and alleviate the burden on healthcare systems. Traditional diagnostic methods often rely on subjective symptom interpretation and delayed laboratory results, which can lead to misdiagnosis or late intervention. ML algorithms, trained on large datasets of clinical symptoms and disease histories, can identify patterns that are not immediately apparent to human practitioners. This project focuses on leveraging ensemble ML techniques to predict diseases based on user-reported symptoms and integrating this prediction with an online appointment booking system. The system aims to bridge the gap between symptom recognition and medical consultation, promoting proactive healthcare.