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Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria

Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria

Jeremiah A. Balogun, Florence Alaba Oladeji, Olajide Blessing Olajide, Adanze O. Asinobi, Olayinka Olufunmilayo Olusanya, Peter Adebayo Idowu
Copyright: © 2020 |Volume: 5 |Issue: 2 |Pages: 20
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781799808381|DOI: 10.4018/IJBDAH.2020070103
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MLA

Balogun, Jeremiah A., et al. "Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria." IJBDAH vol.5, no.2 2020: pp.38-57. http://doi.org/10.4018/IJBDAH.2020070103

APA

Balogun, J. A., Oladeji, F. A., Olajide, O. B., Asinobi, A. O., Olusanya, O. O., & Idowu, P. A. (2020). Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 5(2), 38-57. http://doi.org/10.4018/IJBDAH.2020070103

Chicago

Balogun, Jeremiah A., et al. "Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 5, no.2: 38-57. http://doi.org/10.4018/IJBDAH.2020070103

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Abstract

This study developed a classification model for monitoring the risk of sexually transmitted diseases (STDs) among females using information about non-invasive risk factors. Structured interview with physicians was done in order to identify the risk factors that are associated with the risk of STDs in Nigeria. The model was simulated using the fuzzy logic toolbox accessible in the MATLAB® R2015a Software. The results showed that nine non-invasive risk factors were associated with the risk of STDs among female patients in Nigeria. Two, three, and four 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 namely no risk, low risk, moderate risk, and high risk. The study concluded that the fuzzy logic model approach was adequate for predicting the risk of STDs based on the knowledge of the risk factors.