International Journal of Computer Science and Software Engineering (IJCSSE), Volume 5, Issue 2, February 2016 ISSN (Online): 2409-4285 www.IJCSSE.org Page: 19-23 Clinical Decision Support Systems for Heart Disease Using Data Mining Approach Harpreet Singh 1 and Kuldeep Singh Kaswan 2 1, 2 P.D.M College of Engineering, Bahadurgarh ABSTRACT Now a day’s business is growing at a very rapid pace and a lot of information is generated. The more information we have, based on internal experiences or from external sources, the better our decisions would be. Business executives are faced with the same dilemmas when they make decisions. They need the best tools available to help them. Decision support system helps the managers to take better and quick decision by using historical and current data. By combining massive amounts of data with sophisticated analytical models and tools, and by making the system easy to use, they provide a much better source of information to use in the decision-making process. Health care is also one of the domains which get a lot of benefits and researches with the advent and progress in data mining. Data mining in medicine can resolve this problem and can provide promising results. It plays a vital role in extracting useful knowledge and making scientific decision for diagnosis and treatment of disease. Treatment records of millions of patients have been recorded and many tools and algorithms are applied to understand and analyze the data. Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. A classification based methods in health care is used to diagnose based on certain parameters to diagnosis if the patient have certain disease or not. The purpose is to explore the aspects of Clinical Decision Support Systems and to figure out the most optimal methodology that can be used in Clinical Decision Support Systems to provide the best solutions and diagnosis to medical problems. Keywords: Data Mining, Health Care, Heart Disease. 1. INTRODUCTION Data mining [1, 2] is concerned with finding patterns and models from the available data. Data mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. Data mining or knowledge discovery in database, as it is also known, is the non-trivial extraction of implicit, previously unknown and potentially useful information from the data. This encompasses a number of technical approaches, such as clustering, data summarization, classification, finding dependency networks, analyzing changes, and detecting anomalies [3]. Data mining includes predictive data mining algorithms, which result in models that can be used for prediction and classification, and descriptive data mining algorithms for finding interesting patterns in the data, like associations, clusters and subgroups. 2. DATA MINING IN HEALTH CARE Applications of data mining technology in medical domain include prediction of the effectiveness of surgical procedures and discovery of relationships among medicine and hospital data. In the last few years, the digital revolution has provided relatively inexpensive and available means to collect and store large amounts of patient data in databases containing rich medical information and made available through the Internet for Health services globally. Data mining techniques applied on these databases discover relationships and patterns that are helpful in studying the progression of disease. Data mining plays an important role in medical diagnosis [1]. Neither medicine nor medical reasoning represents exact sciences, thus knowledge, which is hidden in patient records is valuable. Information technologies in healthcare have enabled the creation of electronic patient records obtained from monitoring of the patient visits. This information includes patient demographics, records on the treatment progress, prescribed drugs, lab results, details of examination, , previous medical history, etc. Information system simplifies and automates the workflow of health care institution. Privacy of documentation and ethical use of information about patients is a major obstacle for data mining in medicine. In order for data mining to be more exact, it is necessary to make a considerable amount of documentation. Health records are private