Browsing by Author "Odeniyi Olufemi Ayodeji"

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    DEVELOPMENT OF A PREDICTIVE FUZZY LOGIC MODEL FOR MONITORING THE RISK OF SEXUALLY TRANSMITTED DISEASES (STD) IN FEMALE HUMAN
    (International Research Journal of Engineering and Technology (IRJET), 2020-10-20) Odeniyi Olufemi Ayodeji2, Jooda Janet Olubunmi3, Balogun Monsurat Omolara4, Ajisekola Usman Adedayo5, Idowu Peter Adebayo6; Odeniyi Olufemi Ayodeji; Jooda Janet Olubunmi; Balogun Monsurat Omolara; Ajisekola Usman Adedayo; Idowu Peter Adebayo
    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 MATLAB® 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.