Enhancing Telehealth: Multiple Disease Prediction Through Ensemble Approach
Keywords:
Convolutional Neural network, Data mining, Gated Recurrent Unit, Long short-term memory, Support vector machine, Telemedicine.Abstract
Telemedicine plays a pivotal role in extending healthcare reach through remote consultations, addressing gaps in underserved regions and offering convenience, especially during crises. Data mining techniques in telemedicine extract critical insights from complex medical data, enhancing early disease detection and personalized care. This study presents a novel approach that leverages two hybrid deep learning models (CNN-Bi-LSTM, CNN-GRU) and a stacking ensemble model to predict multiple diseases using telemedicine-derived features. The stacking ensemble utilizes Support Vector Machine (SVM) as its meta-learner. The dataset is sourced from the YBI Foundation's repository, and extensive experimentation showcases the ensemble's superiority, achieving 99.52% accuracy, 99.54% precision, 99.57% recall, and 99.54% F1-score. These remarkable results highlight the potential of unified model architectures in enhancing disease prediction using telemedicine. Beyond advancing predictive healthcare, this research demonstrates ensemble learning's effectiveness in intricate medical datasets, ultimately aiding clinical decisions and patient outcomes.
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