A STUDY ON CLINICAL PREDICTION USING DATA MINING TECHNIQUES V. KRISHNAIAH 1 , G. NARSIMHA 2 & N. SUBHASH CHANDRA 3 1 Assistant Professor, Department of CSE, CVR College of Engineering, Vastunagar, Hyderabad, Andhra Pradesh, India 2 Associate Professor, Department of CSE, JNTUH College of Engineering, Kondagattu, Andhra Pradesh, India 3 Professor of CSE & Principal, Holy Mary Institute of Technology and Science, Hyderabad, Andhra Pradesh, India ABSTRACT This paper can present an overview of the applications of data mining techniques, medical, research, and educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this paper is to evaluate data mining techniques in clinical and health care applications to develop a accurate decisions. The paper also provides a detailed discussion of medical data mining techniques can improve various aspects of Clinical Predictions. KEYWORDS: Decision Making, Medical Records, Data Mining, Decision Tree, Naive Baye, Association Rule, Outpatient Clinic INTRODUCTION The Healthcare industry is among the most information intensive industries. Medical information, knowledge and data keep growing on a daily basis. It has been estimated that an acute care hospital may generate five terabytes of data a year [1]. The ability to use these data to extract useful information for quality healthcare is crucial. Clinical Prediction is a rapidly growing field that is concerned with applying Computer Science and Information Technology to medical and health data. With the aging population on the rise in developed countries and the increasing cost of healthcare, governments and large health organizations are becoming very interested in the potential of Clinical Diagnosis to save time, money, and human lives. “Clinical Prediction is the computerization of medical information to support and optimize (1) administration of health services; (2) clinical care; (3) medical research; and (4) training. It is the application of computing and communication technologies to optimize health information processing by collection, storage, effective retrieval (in due time and place),analysis and decision support for administrators, clinicians, researchers, and educators of medicine.” Computer assisted information retrieval may help support quality decision making and to avoid human error. Although human decision-making is often optimal, it is poor when there are huge amounts of data to be classified. Also efficiency and accuracy of decisions will decrease when humans are put into stress and immense work. Imagine a doctor who has to examine 5 patient records; he or she will go through them with ease. But if the number of records increases from 5 to 50 with a time constraint, it is almost certain that the accuracy with which the doctor delivers the results will not be as high as the ones obtained when he had only five records to be analyzed. Typical problems that data mining addresses are how to classify data, cluster data, find associations between International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 1, Mar 2013, 239-248 © TJPRC Pvt. Ltd.