A multi-variate heart disease optimization and recognition framework

Abstract Cardiovascular diseases (CVD) are the most widely spread all over world among common chronic diseases. CVD represents one of main causes morbidity and mortality. Therefore, it is vital to accurately detect existence heart help save patient life prescribe a suitable treatment. The current evolution in artificial intelligence plays an important role helping physicians diagnose different In present work, hybrid framework for detection using medical voice records suggested. A that consists four layers, namely “Segmentation” Layer, “Features Extraction” “Learning Optimization” “Export Statistics” Layer proposed. first layer, novel segmentation technique based on variable durations directions (i.e., forward backward) Using proposed technique, 11 datasets with 14,416 numerical features generated. second layer responsible feature extraction. Numerical graphical extracted from resulting datasets. third passed 5 Machine Learning (ML) algorithms, while 8 Convolutional Neural Networks (CNN) transfer learning select configurations. Grid Search Aquila Optimizer (AO) used optimize hyperparameters ML CNN configurations, respectively. last output validated performance metrics. best-reported metrics (1) 100% accuracy algorithms including Extra Tree Classifier (ETC) Random Forest (RFC) (2) 99.17% CNN.