###### Abstract  
The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10%. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9-12.9% and 9.1-44.8%, respectively.  
artificial intelligence; fuzzy c-means clustering; generative adversarial network; imbalanced dataset; machine learning; multi-class detection; smart healthcare; synthetic data; voice disorders +
Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://github.com](https://github.com)]([https://github.com](https://github.com))  
likewise drawn to primary and secondary education teachers in Finland: 54% of 1198 teachers in Finland were found to suffer from voice disorders [4]. Voice disorder sufferers are expected to consult Otorhinolaryngologists and speech therapists for diagnosis and medical treatment to support their rapid recovery. However, research has shown that only a small percentage of total sufferers seek professional advice. Only 5.9% (78 participants) sought professional advice when they suffered from voice disorders [5]. Another survey [6] showed that 22.5% (56 teachers) consulted medical advice to solve their voice disorders. Many of the subjects claimed that they did not recognize they were suffering from voice disorders. Indeed, confirming a voice disorder requires medical knowledge.  
Due to the high prevalence and low consultation rate of the population with voice disorders, it is desirable to have an automatic voice disorder detection algorithm with voice inputs that can give an instant diagnosis of a voice disorder, as well as its type. Such algorithms could be integrated into mobile health applications, as demonstrated in related works [7; 8].  
In Section 1.1, the performance of existing works on voice disorder detection is summarized. The limitations of existing works are explained in Section 1.2, which forms the rationale of the proposed work in addressing these limitations. This is followed by the key contributions of this paper in Section 1.3.