MAVSCOT : a fuzzy logic-based HIV diagnostic system with indigenous multi-lingual interfaces for rural Africa

Oluwagbemi, Olugbenga Oluseun ; Oluwagbem, Folakemi Etseoghena ; Jatto, Abdulwahab ; Hui, Cang (2020)

CITATION: Oluwagbemi, O. O., et al. 2020. MAVSCOT : a fuzzy logic-based HIV diagnostic system with indigenous multi-lingual interfaces for rural Africa. PLoS ONE 15(11): e0241864, doi:10.1371/journal.pone.0241864.

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Publication of this article was funded by the Stellenbosch University Open Access Fund


HIV still constitutes a major public health problem in Africa, where the highest incidence and prevalence of the disease can be found in many rural areas, with multiple indigenous languages being used for communication by locals. In many rural areas of the KwaZulu-Natal (KZN) in South Africa, for instance, the most widely used languages include Zulu and Xhosa, with only limited comprehension in English and Afrikaans. Health care practitioners for HIV diagnosis and treatment, often, cannot communicate efficiently with their indigenous ethnic patients. An informatics tool is urgently needed to facilitate these health care professionals for better communication with their patients during HIV diagnosis. Here, we apply fuzzy logic and speech technology and develop a fuzzy logic HIV diagnostic system with indigenous multi-lingual interfaces, named Multi-linguAl HIV indigenouS fuzzy logiC-based diagnOstic sysTem (MAVSCOT). This HIV multilingual informatics software can facilitate the diagnosis in underprivileged rural African communities. We provide examples on how MAVSCOT can be applied towards HIV diagnosis by using existing data from the literature. Compared to other similar tools, MAVSCOT can perform better due to its implementation of the fuzzy logic. We hope MAVSCOT would help health care practitioners working in indigenous communities of many African countries, to efficiently diagnose HIV and ultimately control its transmission.

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