Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when lef untreated. An early diagnosis of arrhythmias would be helpful in saving lives. Tis study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other ffeen classes represent electrocardiogram records of various subtypes of arrhythmias. Te research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. Te dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classifcation, support vector machine (SVM) based approaches including one-against-one (OAO), oneagainst-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. Te SVM method results are compared with other standard machine learning classifers using varying parameters and the performance of the classifers is evaluated using accuracy, kappa statistics, and root mean square error. Te results show that OAO method of SVM outperforms all other classifers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.