Hamza Saad

Explainable Ensemble Machine Learning Framework for Lung Cancer Status Classification: A Retrospective Cross-sectional Study

Machine-learning approaches that combine predictive performance with model interpretability may improve lung cancer status classification. This study aimed to develop and internally evaluate an Explainable Precision Screening Framework for lung cancer risk classification using de...

Explainable Ensemble Machine Learning Framework for Lung Cancer Status Classification: A Retrospective Cross-sectional Study

Non-muscle-invasive bladder cancer (NMIBC) is characterized by a high recurrence rate and a substantial long-term surveillance burden. However, current detection and surveillance strategies rely largely on invasive cystoscopy and urine cytology, which are limited by patient disco...