Cardiac Disease Detection and Classification System using Machine Learning (ML)

In cardiac diagnostics, the application of Magnetic Resonance Imaging (MRI)is crucial.The detection of cardiac structures and anomalies can be improved by the enhancing image contrast.Here, cardiac lesions like tumors, scars, and irregularities in the heart are effectively detected and analyzed by the application of Machine Learning (ML) algorithms.The normal and abnormal tissues can be effectively distinguished by utilizing the classifiers.Early detection (ED) and early treatment was also facilitated by this classifier.In Medical Image Processing (MIP), a novel method that integrates the hybrid optimizations inspired by cetacean behaviors with Sand Cat Swarm Optimization (SCSO), named COA-SCSO was presented in this study.To enhance cardiac MRI Image Qualities (IQ), techniques like Noise Reductions (NR) and Contrast Enhancements (CE) are utilized by these hybrid optimizations, and enhancing cardiac MRI IQ is the objective of these hybrid optimizations.To classify the cardiac conditions using CMRI (Cardiac-MRI) data, the Proximal Support Vector Machine with Generalized Eigenvalue (PSVM-GE) improved by Particle Swarm Optimization (PSO) are used.The benefits of GE-based classifications are used, and it may support the suggested method in detecting patterns from improved cardiac MRI images.For accurate and effective detections of heart conditions, this suggested approach serves as a basis framework.Multidisciplinary approaches may result from the integration of ML methods with optimizations, and it will enhance Medical IQ.Contrast Enhancement (CE), NR, are facilitated by the suggested COA-SCSO model, and this model also enhances classification performance.The reliable and accurate cardiac anomalies detection was ensured by this suggested model.The Clinical Decision-Making (CDM) in cardiology was then improved by the study, and it was demonstrated by the outcomes.This will contribute an effective Computer-Aided Diagnostic (CAD) systems.