This study emphasizes the classification of different cardiac diseases through bio-inspired classifiers with and without hyperparameters selection. Principal Component Analysis (PCA), Linearity Preserving Projection (LPP), Kernel-Linear Discriminant Analysis (K-LDA), and Variational Bayesian Matrix Factorization (VBMF) are used for the dimensionality reduction of ECG Signals. After the dimensionality reduction, the ECG features were classified using several classifiers such as Particle Swarm Optimization (PSO), Fish Swarm Optimization (FSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Support Vector Machine with (RBF (kernel)), K Nearest Neighbor (KNN) and Naïve Bayesian Classifier (NBC). The average accuracy for the classifiers without hyperparameters selection is 64.99%. Further, the classifiers (PSO, FSO, WOA, and GWO) performance is enhanced through the selection of hyperparameters by the Adam and Randomized Adam (R-Adam) approaches. The average accuracy for the classifiers with Adam and R-Adam is at 72.32% and 85.63%. In this research, the MIT-BIH datasets of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), ST Change (ST), Supra Ventricular Tachycardia (SVT), Ventricular Tachycardia (VT) and Normal Sinus Rhythm (NSR) are utilized from the physionet archived database. The Overall Accuracy (OA), Mean Square Error (MSE), F1-Score (F1), Fowlkes Mallows Index (FM), Mathew Correlation Coefficient (MCC) and Error Rate (ER) of the different classifiers with and without Adam and R-Adam methods are evaluated. The result demonstrates that the GWO-R-Adam classifier with the LPP dimensionality reduction technique with 98.90% overall accuracy in classifying {ST}-{NSR} classes outperforms all other classifiers in terms of classification performance measures.