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There are several physical and social factors that are associated to the maternal health and may be considered influential towards the C-Section across the world. Several studies have been conducted in different regions of the world, mostly targeting the pregnant women in specific region. The dynamicity of pregnancy and differences among women with respect to region and social life enforces the researchers to widen the sphere of research at regional level to comprehensively explore the significant risk factors associated to mother and expected child. In this paper, the region of interest is the native city of authors lacking medical facilities and proper pregnant women healthcare infrastructure. As compared to advanced countries, no such study is ever conducted in this region that involves cause analysis of factors resulting in enhanced cases of C-sections and assisting physicians via providing decision support systems based on knowledge induced from machine learning approaches. The aim of this paper is twofold. The first objective is to collect data regionally, in order to conduct local study, first of its kind in this region, and acquire results that are helpful for public health offices in decision making. Second, it is desired to produce different birth classification models and study their applicability on birth data collected previously. The best approach on the basis of correct classification may later be used to produce decision support systems to assist physicians to gain knowledge from the hidden patterns in data. The success of such study is crucial as it will open the doors of interdisciplinary research in two distinct fields of the region.

Cause Analysis of Caesarian Sections and Application of Machine Learning Methods for Classification of Birth Data