OG-Miner: An Intelligent Health Tool for Achieving Millennium Development Goals (MDGs) in m-Health Environments

The latest statistics of WHO show that approximately 500, 000 women die worldwide every year - the majority of them residing in developing countries - due to pregnancy related complications. The situation is so grave that UN has set a target of reducing Maternal Mortality Rate (MMR) by 75% till the year 2015 in its millennium development goals (MDGs). Therefore, the current focus of health care researchers is to advocate the use of e-health technology in developing countries that have the capability: (1) to remotely monitor patients in their homes by semiskilled health professionals, and (2) to use data mining techniques to raise alarms about high risk patients. In this paper, we develop an intelligent health tool - Obstetrics and Gynaecology (OG) OG-Miner - that presents a novel combination of data mining techniques for accurate and effective classification of high risk pregnant women. The scheme classifies four major risk factors of mortality - hypertension, hemorrhage, septicemia and obstructed labor - in a reliable, autonomous and accurate fashion. We have collected a real world data of more than 1200 patients from tertiary care hospitals and rural areas. Our tool achieves more than 98% accuracy on the collected OG dataset. Moreover, our evaluations of OG-Miner on eight other medical datasets show that its learning paradigm can be generalized to other domains as well. Last but not least, we are using OG-Miner as an integral component of a health value chain in our m-health project to autonomously filter a significant number of low risk patients in rural areas; as a result, only high risk patients are referred to specialized obstetrician in tertiary care hospitals. As a consequence, the reduced workload enables them to provide quality care to the patients.