Malaria is a life-threatening disease in Nigeria and it is caused by the bite of a female anopheles mosquito. Malaria is increasing in an uncontrolled way but its diagnosis is still at a very poor state in Nigeria. The World Health Organization (W.H.O) reported that an estimate of fifty million children in Africa died of malaria from the years 2015 to 2019. In this study, we developed an Enhanced Malaria Diagnostic Model using Artificial Bee Colony (ABC) algorithm. Structured Analysis and Design Technique (SADT) was adopted as methodology, and we further implemented with Hypertext Preprocessor (PHP), and MySQL. In addition, the Enhanced Malaria Diagnostic Model will be beneficial to doctors and specialists in life-threatening disease such as malaria, and the Nigerian Centre for Disease Control (NCDC).

Keywords : Artificial Bee Colony (ABC); Diagnosis; Malaria; SADT.