Description

Medicinal data mining methods are used to analyze the medical data information resources. Medical data mining content mining and structure methods are used to analyze the medical data contents. The effort to develop knowledge and experience of frequent specialists and clinical selection data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. Diagnosis of heart disease is a significant and tedious task in medicine. The term Heart disease encompasses the various diseases that affect the heart. The exposure of heart disease from various factors or symptom is an issue which is not complimentary from false presumptions often accompanied by unpredictable effects. Association rule mining procedures are used to extract item set relations. Item set regularities are used in the rule mining process. The data classification is based on MAFIA algorithms which result in accuracy, the data is evaluated using entropy based cross validations and partition techniques and the results are compared. Here using the C4.5 algorithm as the training algorithm to show rank of heart attack with the decision tree. Finally, the heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database. The results showed that the medicinal prescription and designed prediction system is capable of prophesying the heart attack successfully.