Abstract:
Hepatitis B is one of the liver diseases that is difficult to
discover at an early stage of its attack and prominent public
health problem. As at 2017, medical statistic recorded that
over 23 million of Nigerians were living with Hepatitis B.
Several decision support systems used in diagnosing liver
diseases derived their efficiencies from artificial intelligence
techniques in tackling the challenges facing physician in
respect to complexity of the numerous variables involved in
liver diseases diagnosis. In this paper, Adaptive Neuro-Fuzzy
Inference System (ANFIS) was employed to invoke neural
network that provided structures for fuzzy inference engine
(FIE) in order to learn information about the normalized
dataset on hepatitis B. The neural network (NN) triggers
backpropagation and least square methods for tuning the
membership functions at the fuzzification stage while the
center of area (COA) was used as defuzzification method to
compute the weighted average of the fuzzy set and intensity
level of the disease for each record. The system was
implemented with technical computing language,
MATHLAB, on a dataset that consists of 155 instances and 20
attributes of which only the most five liver function tests
(LFTs) attributes were selected as input parameters and the
corresponding linguistic values and intensity levels were
generated as output in order to identify the severity level of
the infection. After the system was evaluated, the performance
metric gave accuracy of 90.2%.
