Automated Cardiac Disease Diagnosis Using Support Vector Machine

Abstract

This research is intended for the creation of an automatic cardiovascular disease detection system with pre-marked SPECT (Single photon emission computed tomography)data for training the program and for subsequently supplying it with unclassified SPECT data for classification as an indication of probable heart disease or not. We used a well-known and widely used classification algorithm named Support Vector Machines (SVM) for the purposes of this classification. In other terms, you instruct the machine; the system learns from the inputs you give. When the training is over, you can provide the system with undefined SPECT data and it is immediately identified by the system Clearly, the accuracy of this classification would depend on the precision of the pictures of the training system, the support vector machine parameters, the sophistication and many more variables that are discussed later. The project is of considerable significance in today's world, particularly if businesses offer a million-dollarreward to anyone capable of building automated systems detecting nearly every Star Wars (such as transcoder) disease.