SHIELD: Symptom-Based Hybrid Intelligent Early Learning for Disease Prediction - Fazil Akashah, 'Asrul 'Azeem; Tajuddin, Taniza (IC-ITECHS, 2024)
Traditional diagnostic approaches often face delays and inaccuracies, while standalone machine learning models fail to account for individual uniqueness. The SHIELD system leverages hybrid machine-learning models to enhance disease prediction based on patient symptoms. This study integrates Gradient Boosting, Decision Trees, and Random Forest models, combining their strengths using an ensemble voting approach. A comprehensive dataset from Kaggle, enriched with symptom severity mappings, enables accurate and personalized predictions. The system delivers practical outputs, including disease names, descriptions, and home remedies, through a user-friendly web interface. Achieving an accuracy of approximately 99.59% with the ensemble model, SHIELD demonstrates its potential to revolutionize early disease detection, aligning with global health objectives.