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 (Mountain Top University, Nigeria), Florence Alaba Oladeji (University of Lagos, Nigeria), Olajide Blessing Olajide (Federal University, Wukari, Nigeria), Adanze O. Asinobi (University of Ibadan, Nigeria & University College Hospital, Ibadan, Nigeria), Olayinka Olufunmilayo Olusanya (Computer Science Department, Tai Solarin University of Education, Nigeria) and Peter Adebayo Idowu (Department of Computer Science and Engineering, Obafemi Awolowo University, Nigeria)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/IJBDAH.2020070103


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.
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1. Introduction

Sexually transmitted diseases (STDs) are infections that can be transferred from one person to another during sexual activity (Bryan, 2011). In developing countries like Nigeria, STDs exhibit a higher incidence and prevalence rates than developed countries (Adebowale, Titloye, Fagbamigbe, & Akinyemi, 2013). STDs remain a major public health challenge because of their health consequences and severe complications, especially among women who excessively bear their long-term consequences. Among, the most popular STDs are HIV/AIDS and Syphilis (Lakshmi and Isakki, 2017). HIV is a human immunodeficiency virus. It is the virus that can lead to acquired immunodeficiency syndrome or AIDS if not treated (Ojunga, Peter, Otulo, Omollo, Edgar, 2014). Others include chlamydia, cancroid, pubic lice (crabs), genital herpes, hepatitis B, scabies, human papillomavirus (HPV), gonorrhea etc.

The Nigerian Health sector has set ambitious targets for providing essential health services to all citizens; improving the quality of decisions affecting treatment options for people at risk of STDs is very essential to reducing disease risk in Nigeria. Across the world, the number of cases of sexually transmitted diseases (STDs) are continuously on a steady increase due to the number of unprotected sex, lack of quality and timely sexual education and on most part ignorance. The need for an effective means of reducing the number of associated cases of STDs is necessary owing to the steady increase in the number of sex-related professions, such as sexual workers alongside those who engage in unprotected sex to mention a few.

Information and Communications Technology (ICT) has the capability of improving the quality, efficiency and safety of health care and allows health care providers to collect, store, retrieve, and transfer information by electronic means (Shekelle, Morton, & Keeler, 2006). Predictive research, which aims to predict future events or outcomes based on patterns within a set of variables, has become increasingly popular in medical research. Accurate predictive models can inform patients and physicians about the future course of an illness or the risk of developing illness and thereby help guide decisions on screening and/or treatment (Waijee, Higgins, & Singal, 2013).

Expert systems (also known as knowledge-based systems) are computer programs that aim to achieve the same level of accuracy as human experts when dealing with complex, poorly structured problems in a particular area, which allows non-specialists to use them for receiving answers to problems or for experts to gain decision support (Turban, Sharda, & Delen, 2010). The strength of these systems lies in their ability to use expert knowledge almost when an expert is unavailable. Expert systems make knowledge more accessible and help solve the problem of translating knowledge into practical useful results. As a result of this, fuzzy logic is used mainly to eliminate the uncertainties in human-oriented analysis as a way of processing complex, inaccurate, uncertain, and vague data.

Fuzzy logic is an extended set of traditional (Boolean) logic designed to access the concept of partial truth (Massad, Ortega, de Barros, & Struchiner, 2008). Fuzzy logic is associated with the morphology of logical inference, which can apply the approaches of human thinking to knowledge-based systems. Fuzzy logic is a computational approach based on degrees of truth, and not on the classic truth or false Boolean logic (1 or 0). This is because it is difficult to assign a natural language in absolute terms 0 and 1. Fuzzy logic uses 0 and 1 as extreme cases of truth, but also contains various states of truth represented by the meanings that lie between them. Fuzzy logic was introduced in order to solve the problem of imprecision and uncertainty, so as to improve tractability, robustness and low-cost solutions for real world problems (Sharareh and Xiao-Jun, 2009).

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