Classification of Complete Myocardial Infarction Using Rule-Based Rough Set Method and Rough Set Explorer System.

In this study, a computerized diagnosis system is developed using Rough set classifier from multi-lead ECG signal for detection as well as the classification of five different types of myocardial infarction (MI) disease. The pathological features of ECG such as Inverted T-wave, ST segment deviation, or pathological Q wave, which are seen during MI, are extracted. An Information table and the knowledgebase are expanded from these pathological features after getting feedback from the cardiologist as well as consulting different medical books. The Information table contains 36 features and 341objects which include normal and five types of MI such as Anterior (AN), Inferior (IN), Antero lateral (ANLA), Inferior lateral (INLA), and Antero septal (ANSE) are used for assessment. The proposed system determines the degree of attributes dependency and their significance to find a smaller set of attributes, called reduct, alike the original set to predict the appropriate decision rules for MI classification. The robustness is justified by the "five-fold cross" validation technique using RSES tools. Finally, the proposed classifier illustrates its outperformance over the existing approaches in terms of sensitivity (99.75%), and accuracy (99.8%) for MI detection and 99.8% accuracy for MI classification. [ABSTRACT FROM AUTHOR]