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.