Ensemble Learning-Based Seizure Classification with Quality Assurance and Regulatory Alignment in AI-Driven Healthcare

Artificial intelligence (AI) is increasingly transforming healthcare, especially in diagnosing and managing neurological disorders such as epilepsy. Precise classification of seizures is crucial for prompt and effective intervention. This study presents a new ensemble learning framework that combines several machine learning models, such as Random Forest, Support Vector Machines, XGBoost, and LightGBM, along with an adaptive Quality Assurance (QA) module for finding seizures in EEG signals. Experiments on the Bonn EEG dataset reveal a notable enhancement in classification performance, with accuracy rising from 72 % to 90 % following QA inclusion. The QA module diminished high-risk seizure misclassifications by almost 95%, underscoring its capacity to enhance clinical safety. The framework incorporates ideas from recognised healthcare standards, including ISO 14971, IEC 62304, and FDA Good Machine Learning Practices (GMLP); however, complete regulatory compliance is an objective for future development. Current efforts involve improving validation among varied patient demographics, improving real-time processing functionalities, and investigating interaction with telemedicine and cloud-based healthcare infrastructures.