Abstract
Risk management in the development of medical software and devices is one of the most crucial processes in ensuring accurate diagnoses and treatment of disease. The consequences of wrong decisions that happen in our daily life might be unembellished. However, wrong decisions in healthcare based on unreliable evidence due to erroneous software could result in loss of life. Dysphonic patients suffering from various vocal fold disorders might have a threat of life due to inaccurate diagnosis. Some voice disorders, such as keratosis, are precancerous, and can become cancerous in cases that involve inaccurate diagnosis due to software failure. The objective of this paper is to design and implement a healthcare software for the detection of voice disorders in nonperiodic speech signals. Occurrences of potential risks during the design and development of the proposed software are taken into account to avoid failure. The software is implemented by applying the local binary pattern (LBP) operator on the textures of nonperiodic signals. The textures are obtained through the recurrence plot. The LBP operator computes the histograms for normal persons and dysphonic patients, and these histograms are used with the support vector machine for the automatic classification of dysphonic patients. The software is evaluated and tested by using the Massachusetts Eye and Ear Infirmary voice disorder database. The success rate of the proposed healthcare system is 97.73% ± 1.2, and the area under the receiver operating characteristic curve is 0.98 ± 0. The performance of the proposed healthcare system is much better than the existing commercial software used for screening dysphonic patients.