Ethiopic maternal care data mining: Discovering the factors that affect postnatal care visit in Ethiopia

Abstract

Background: Improving maternal health and reducing maternal mortality rate are key concerns. One of the eight millennium development goals adopted at the millennium summit, was to improve maternal health in Ethiopia. This leads towards discovering the factors that hinder postnatal care visit in Ethiopia. Methods: In this research, knowledge discovery from data (KDD) was applied to identify the factors that hinder postnatal care visits in Ethiopia. Decision tree (using J48 algorithm) and rule induction (using JRip algorithm) techniques were applied on 6558 records of Ethiopian demographic and health survey data. To construct essential target dataset attributes exploratory data analysis with frequency diagram is performed, missing value was filled and noisy value was corrected. Also the data are preprocessed using business and data understanding with detail statistical summary. Result: J48 (93.97 % accuracy) and JRip (93.93 % accuracy) identifies places of delivery, assistance of health delivery professional, prenatal care health professional and age are the determinant factors. However, residence places also taken into consideration. Conclusions: In this study, encouraging results were obtained by employing both decision tree and rule induction techniques. The rules generated by J48 and JRip algorithms are much understandable to explain the outcome easily. Thus, the result obtained highly supportive to construct, evaluate and update advertising and promotional maternal health policies. It is better to create a generic model with more coverage in terms of economic, demographic, social and genetic factors so as to integrate the result with knowledge based system.