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In most of the developing countries, the rural population is denied of the efficient and effective health care facilities. This increases the mortality of people in very young age due to several unknown and untreated diseases. This situation can be improved by adopting the usage of wearable sensors that are capable of continuously monitoring the patients and issue warnings to specialized experienced doctors in hospitals or to the care takers. This approach will bring in better healthcare facility to the people living in the rural world or to the people who are unnecessarily staying in the hospitals just for the purpose of monitoring. This can also help those people who does not want to stay in hospitals. However the efficiency of such a system will depend on the capability of the decision support system integrated with it. Hence this research work aims at the development of a decision support system architecture that can support data collection and processing from multiple wearable sensors. The real-time data received from multiple wearable sensors will be analysed for a variety of diseases. The results will be stored and send to the required persons. As an initial step towards the development of decision support system, a prototype system is developed that can be used for the monitoring of cardiac diseases such as
Ischemia,MyocardialInfraction,Cardiomyopathy,Wolff-Parkinson-WhiteSyndrome,Hypokalaemia, Hyperkalaemia and First degree AV block. This work has also developed a new risk based scheduling algorithm to handle the data processing so that the patients with the highest risk are processed first. The work also includes the implementation of several techniques such as decision trees and Support Vector Machines for taking better decisions and for proper classification of diseases.
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