Meshesha, Million (PhD)Shikur, Bilal (PhD)Beyene, Tigist2018-11-192023-11-292018-11-192023-11-292015-06http://etd.aau.edu.et/handle/123456789/14407Background: Nutritional status is the result of complex interaction between food consumption and the overall status of health and health care practices. Women of the reproductive age (15-49) are amongst the most vulnerable to risk of malnutrition. In 2011, among adults, 500 million women were anemic, and 500 million women were obese. Objective: The main objective of this study is to apply data mining technique for constructing a predictive model that helps to predict nutritional status of women of reproductive age in Ethiopia. Method: This study used a Hybrid data mining model and the dataset was extracted from the most Ethiopian Demographic and Health survey. To run the experiment used 18875 records, eighteen predicting variables and one outcome variable. Classification mining techniques are selected to build the model. Because of the their popularity in recent works J48, decision tree, PART rule induction, MLP Neutral Network, SMO support vector machine and naïve bayes algorithms were used as they implemented in Weka 3.6.10 tool. Result: The best classification result, and a better predictive accuracy of women nutritional status was obtained from the unpruned PART rule induction. The experiment generated a model with accuracy of 83.14%, weighted precision of 83.1% and Weighted ROC area of 90.7%. Women’s age, socioeconomic status, educational level sources of drinking water, latrine facility, breast feeding status, occupation, contraceptive method being under use, marital status, anemia level, residence and region, are the determinant factors of women’s malnutrition. Conclusion: The current result indicated that data mining is advantageous in bringing relevant information from large and complex dataset which can used for decision making. Program implements might also use the finding to identify women which needs special attention to reduce malnutrition.enWomen of Reproductive Age in EthiopiaThesis