In recent years, the global medical community has endeavored to provide swift and efficient patient care by leveraging real-time patient databases. However, the efficacy of these systems, particularly wireless body area network (WBAN) sensors, has been undermined by inaccurate and low-performance readings, leading to unnecessary alarm triggers. This study scrutinizes the potential of data dimensionality reduction techniques and machine learning algorithms in augmenting the detection accuracy of cardiac abnormalities in WBAN sensors. Dimensionality reduction was performed using principal component analysis (PCA), independent component analysis (ICA), and spatial correlation methods. For arrhythmia prediction, Decision Tree and Multilayer Perceptron algorithms were implemented and their performance compared. Numerical simulations and Python code analysis revealed that the application of data reduction techniques significantly improved the reliability and effectiveness of WBAN sensors in handling voluminous datasets. Furthermore, the use of PCA, ICA, and spatial correlation strategies notably reduced WBAN sensor battery energy consumption, data storage needs, computational complexity, and processing time. These pragmatic solutions could potentially empower healthcare practitioners to intervene proactively before patients encounter life-threatening conditions. The results also demonstrated that feature selection effectively eliminated irrelevant attributes from noisy Electrocardiograms (ECGs), thereby enhancing the precision of the analyses.
