Application of Data Mining to Predict the Likelihood of Contraceptive Method Use among Women Aged 15‐49

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Addis Ababa University


In Ethiopia a gap between knowledge and use of contraceptive method is observed from many studies. According to the 2005 Ethiopian Demographic Health survey report the knowledge about any modern method among women is 86%, Contraceptive Acceptance Rate is 50.1% whereas the Contraceptive Prevalence Rate is 13.9%. Therefore the main objective of this study is to predict the likelihood of contraceptive method use among women aged 15-49 based on demographic, socio-economic, geographic, reproductive history and knowledge factors using data mining classification techniques in order to tackle the barrier factors of contraceptive method use and increase the prevalence of contraceptive method use of those women with low likelihood of practicing family planning by working on the factors. In order to find and interpret patterns from data the KDD process model is employed. This has gone through the steps of the process model; data selection and understanding, preprocessing, transformation, data mining, interpretation and evaluation. Decision tree and Naïve Bayes are used for the purpose of classification. The dataset used in this study is the 2005 demographic health survey data collected by central statistics agency. The techniques are tested both on the balanced and unbalanced datasets. Experimental results show that J48 decision tree performs better than Naïve Bayes. From this model 253 rules are generated. One important rule detected was; women who do not know any contraceptive method have no any chance of using contraceptive method. But having knowledge of contraceptive method could not be a guarantee in order to use contraception. Other factors such as Partner occupation, partner education level, wife’s education level, FP message, wealth index, Visit by FP worker were found to be most determinant factors as well. It is therefore recommended all concerned parties to strengthen the promotion of contraceptive method knowledge, improve both partner and wife education level. FP message and Visit by FP workers are also important in increasing CM use. KEYWORD: Data Mining, Family Planning, Contraceptive Method, Decision Tree, Naïve Bayes



Data Mining, Family Planning, Contraceptive Method, Decision Tree, Naïve Bayes