Published Paper Details:

The healthcare industry collects a large amount of data that is not properly mined and not optimally used. Discovery of these hidden patterns and relationships often goes unexploited. Information and communication technologies (ICTs) have great potential to improve health in both developed and developing countries by enhancing access to health information and making health services more efficient; they can also contribute to improving the quality of services and reducing their cost. Patient information systems, for example, have the ability to track individual health problems and treatment over time, giving insight into optimal diagnosis and treatment of the individual as well as improving the delivery of services. This is particularly useful in chronic diseases, such as diabetes and cardiovascular diseases, as well as health services for mothers and children, where a record of health and treatment is required within a given time period. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of diabetes and heart diseases and to develop intelligent medical decision support systems to help the doctors. Analyzing data in patient information systems can lead to a new insight and understanding of health and disease, both chronic and acute. In this paper, we propose the use of decision trees ID3 algorithm classify these diseases and compare the effectiveness, correction rate among them.