Fast k-NN classification for multichannel image data
Pattern Recognition Letters
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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Reducing infant mortality is one of the primary Millennium Development Goals 2015. A lot of effort has been made to reduce infant mortality but it remains high in most of the developing countries and the underdeveloped world. Perinatal Mortality is a cause of great emotional pain and social unrest. The main cause of pregnancy failure in the developed world is obesity but in the under-developed world the main cause remains malnutrition. However, their are a mix of factors that affect pregnancy failure in the developing countries. Pakistan has a very high infant mortality rate which stands at 78 deaths per 1000 births. The reasons for this are many including lack of proper healthcare. This is because of a severe shortage of healthcare professionals and specialists in Pakistan. The gap in healthcare may be overcome by leveraging IT to provide automated healthcare. In this paper, we show how machine learning may be used to predict perinatal failure. We examine the relationship between pre-pregnancy weight, weight gain during pregnancy and the body mass index (BMI) to investigate how they relate to foetal failure. We employ the K Nearest Neighbor (K-NN) technique to automatically differentiate between successful and failed pregnancies. Our method is able to predict the the outcome of a pregnancy with about 95% accuracy.