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.