Work place: Department of Pharmacy & Medicine, College Station, Texas A&M University, TX, USA
E-mail: surani@tamu.edu
Website: http://orcid.org/0000-0001-7105-4266
Research Interests:
Biography
Dr. Salim Surani serves as Clinical Professor at the University of Houston. In addition, he served as a Professor at Texas A&M University for two decades and is currently a Research Collaborator at the Mayo Clinic in Minnesota. He has served as the program director of the Pulmonary Fellowship Program at Bay Area Medical Center in Corpus Christi. He completed his fellowship in Pulmonary Medicine at Baylor College of Medicine in Houston, Texas. Dr. Surani has a master’s in public health & Epidemiology from Yale University and a master’s in health management from the University of Texas at Dallas. Dr. Surani also served as a board trustee for THE CHEST Foundation and as Chair of the practice operations committee. In addition, he serves as a member of the Board of Advisors, Chair of the Chest Infection & Disaster Prevention network, a Board of Advisors member for the American College of Chest Physicians, and Chair of the FCCP subcommittee for CHEST. He has also served on numerous national and international committees. He serves as editor-in-chief, editorial board member, and reviewer for several peer-reviewed journals. Dr. Surani has authored more than 500 peer-reviewed journal articles, with more than 26,000 citations, and has written more than 100 book chapters. He has established himself as a Master Clinician who has trained many practicing primary care, internal medicine, and emergency physicians in the USA's Coastal Bend region. He has served as the morning news health segment expert for KIII-TV, an ABC News affiliate, for 14 years. Dr. Surani is highly regarded among his peers and is very well respected as a mentor, clinician, and humanitarian.
DOI: https://doi.org/10.5815/ijeme.2026.05.02, Pub. Date: 8 Oct. 2026
Obstructive Sleep Apnea (OSA) is a well-known sleep disorder that can lead to major health consequences if
left untreated. The traditional diagnostic method, polysomnography, is precise but costly and labor-intensive. This research presents an Evolutionary Feedforward Neural Network (FFNN) optimized using a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Crow Search Algorithm (CSA) to diagnose OSA. We utilized a dataset comprising demographic and physical measurements. The adopted features include age, sex, weight (lb), height (in), neck circumference, body mass index (BMI > 30), (Neck > 17), airway evaluation via the modified Friedman grade (MF > 2), the Berlin apnea-hypopnea questionnaire (BAN), and the identification of apnea as indicated by the apnea hypopnea index (AHI ≥ 15). We used this methodology to optimize the FFNN’s weights and biases, improving generalization and avoiding local minima that traditional backpropagation can trap the model in. The results indicate that GA, PSO, and CSA achieved competitive, comparable performance, with PSO showing a marginally better performance across most evaluation metrics, rather than a significant superiority of any single optimization method.
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