Naïve Bayes Based Diagnostic System for Ear, Nose and Throat (ENT) Diseases

David Emmanuel Aniobi, Daniel Aondoaver Tsav, Pat-Mercy Nnenna Aniobi, Esther Benisemeni Zakka


Diseases are aberrant health disorders that makes living uncomfortable and need to be properly addressed when they do occur. Otolaryngology/Ear, Nose, and Throat (ENT) illness problems can impair a person's ability to smell, hear, speak, learn, or perform crucial functions in life. The services of ENT specialists are not always easily accessible in some parts of the country. Therefore, effectiveness and efficiency of this healthcare system can be improved by using a Computer-Aided App that can help Otolaryngologists and patients diagnose and treat ENT related diseases. This study proposes a Multi-Class ENT Medical Diagnostic System (MCEMDS) that can help experts and non-experts to diagnose ENT-related disorders using the Naive Bayes Algorithm. The system was implemented using PHP, Java script, and MySQL. The findings of the App's testing revealed that the system was capable of diagnosing a number of ear, nose, and throat conditions indicating the likelihood of their occurrence. The system has high accuracy (>90%) and it is consistent and reliable for ENT diseases detection.



Diagnostic system; ENT diseases; Intelligence; Naïve Bayes; Otolaryngology.

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