Medical data mining and analysis for heart disease dataset using classification techniques

Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and making scientific decision for diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also improve the management level of hospital information and promote the development of telemedicine and community medicine. Medical field is primarily directed at patient care activity and only secondarily as research resource. The only justification for collecting medical data is to benefit the individual patient. The main theme of this paper is to store medical information of patients who come for hospitalization for heart disease and algorithms are run on that information and result will be provided in the form of user understandable words and graph. When very large data sets are present, data mining algorithms (here considering only ID3 and Naïve Bayesian algorithms) are used. ID3 outputs the result in the form of decision tree which can be easily understood. Naïve Bayesian predicts the chances of heart disease based on conditions given.