DEVELOPMENT OF A PREDICTIVE FUZZY LOGIC MODEL FOR MONITORING THE RISK OF SEXUALLY TRANSMITTED DISEASES (STD) IN FEMALE HUMAN
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Date
2020-10-20
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Journal ISSN
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Publisher
International Research Journal of Engineering and Technology (IRJET)
Abstract
The purpose of this study is to develop a
classification model for monitoring the risk of sexually
transmitted diseases (STDs) among females using
information about non-invasive risk factors. The specific
research objectives are to identify the risk factors that
are associated with the risk of STDs; formulate the
classification model; and simulate the model. Structured
interview with expert physicians was done in order to
identify the risk factors that are associated with the risk
of STDs Nigeria following which relevant data was
collected. Fuzzy Triangular Membership functions was
used to map labels of the input risk factors and output
STDs risk of the classification model identified to
associated linguistic variables. The inference engine of
the classification model was formulated using IF-THEN
rules to associate the labels of the input risk factors to
their respective risk of still birth. The model was
simulated using the fuzzy logic toolbox available in the
MATLABĀ® R2015a Simulation Software. The results
showed that 9 non-invasive risk factors were associated
with the risk of STDs among female patients in Nigeria.
The risk factors identified were marital status, socioeconomic
status, toilet facility used, age at first sexual
intercourse, practice sex protection, sexual activity (in
last 2 weeks), lifetime partners, practice casual sex and
history of STDs. 2, 3 and 4 triangular membership
functions were appropriate for the formulation of the
linguistic variables of the factors while the target risk
was formulated using four triangular membership
functions for the linguistic variables no risk, low risk,
moderate risk and high risk. The 2304 inferred rules
were formulated using IF-THEN statements which
adopted the values of the factors as antecedent and the
STDs as consequent part of each rule. This study
concluded that using information about the risk factors
that are associated with the risk of STDs, fuzzy logic
modeling was adopted for predicting the risk of STD
based on knowledge about the risk factors.
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Citation
12. Olajide Blessing Olajide, Odeniyi Olufemi Ayodeji, Jooda Janet Olubunmi, Balogun Monsurat Omolara, Ajisekola Usman Adedayo, & Idowu Peter Adebayo (2020). Development of Predictive Fuzzy Logic Model for Monitoring the Risk of Sexually Transmitted diseases (STD) in Female Human. International Research Journal of Engineering and Technology (IRJET), 7(3), 4666-4676.