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Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring. |
