Browsing by Author "Abdulsalam S. O."

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    Effectiveness of Contraceptive Usage among Reproductive Ages in Nigeria Using Artificial Neural Network (ANN)
    (University of the West of Scotland, School of Computing, 2018) Dauda K. A.; Babatunde A. N.; Olorede K. O.; Abdulsalam S. O.; Ogundokun O. R.
    Purpose: Contraceptive usage among women of reproductive age is a fertile area of research for the medical scientists, social scientists, and medical practitioners. However, not much work has been done in Nigeria to identify some of the contraceptive methods used in preventing unwanted pregnancy and sexually transmitted infection (STI) diseases, particularly to explicitly determine which of them are most effective. This paper tackles this task. Methodology: In this paper, we examine different types of contraceptive methods used by Nigeria women to prevent unwanted pregnancy. Datasets on contraceptive usage among 28,647 women of reproductive age from the 2008 Nigerian Demographic Health Survey (NDHS) were analyzed using Artificial Neural Network (ANN) approach in the R language. Finding/Results: The results of the analysis revealed that approximately 1%, 3% and 9% of the study population were using Folkloric, Traditional and Modern methods, respectively. Additionally, 12%, 3% and 9% of the women were found to be currently using the methods since last birth and before last birth. Furthermore, the ANN results through the Garson algorithm revealed that using the modern methods (IUD, Norplant and Female condom) and the traditional methods prevents the unwanted pregnancy negatively, while other modern methods (pill, male condom, female sterilizer, periodic abstinence, withdrawal, locational amenorrhea and foam or jelly) were found to be effective in preventing unwanted pregnancy positively. Interestingly, women that used the modern methods (i.e. Pill, IUD, Injection, Male condom, Female sterilization, Periodic abstinence, Withdrawal and Lactational amenorrhea) were found to be effective in preventing unwanted pregnancy. Research Limitation: The main limitation of this study is the inability to access the current data from NDHS. Therefore, the conclusion of this study is only based on the 2008 NDHS data. Originality/Value: This research work introduces artificial neural network (ANN) to determine and identify the effect of contraceptive methods usage among Nigeria women, and to determine their level of important using Garson's algorithm variable important. This introduction measures the nonlinear effect that exists between the methods and response variable (pregnant and no pregnant) which existing research approaches do not. Keywords: Contraceptive methods, Variable Important, Garson Algorithm, Artificial Neural Network (ANN) and Data mining.