Accurate and Low-Cost Cardiac Disorder Detection from Wearable Phonocardiogram Signals Using Hybrid Feature Selection and Machine Learning.

Early and reliable identification of cardiac disorders from Phonocardiogram (PCG) signals acquired from wearable biosensors is critical to support clinical decision making and reduce subjectivity in auscultation-based assessments. This study proposes a multi-stage hybrid feature selection-classification approach to increase diagnostic accuracy without requiring computationally expensive deep learning (DL) architectures. First, the most statistically discriminative features were identified using mRMR, ReliefF, and Kruskal-Wallis filtering methods. Particle swarm optimization (PSO) and ant colony optimization (ACO) were then applied to optimize the solution space. Finally, the selected feature subsets were tested with k-nearest neighbor (k-NN), support vector machines (SVMs), and Bagged Tree (BT) classifiers. Experimental results show that the proposed method significantly increases the model robustness and generalizability. In particular, the Kruskal-Wallis+k-NN and ReliefF+k-NN combinations achieved competitive performance compared to many DL-based approaches in the literature, with 99.80% accuracy and 99.50% F1-score. Furthermore, hybrid models augmented with PSO and ACO also achieved 99.60% accuracy. The findings demonstrate that well-designed feature selection strategies offer high accuracy and enhanced clinical applicability while using only a small set of handcrafted features and conventional classifiers. Therefore, the proposed framework is a strong candidate for smart stethoscope-based early screening solutions.