A processor implemented method for identifying cardio-vascular abnormality in Electrocardiogram and phonocardiogram by constructing a generalized one-class support vector machine with jointly optimized hyperparameters thereof, the method comprising: constructing a generalized one-class support vector machines (OC-SVM) by jointly optimizing hyperparameters (i) kernel co-efficient γ and (ii) rejection rate hyperparameter v, corresponding to a maximum performance P max of the OC-SVM, wherein P max is identified from a matrix which consists of all the possible combinational values of hyperparameters γ and v and where each element corresponds to the performance of OC-SVM, wherein the step of jointly optimizing the hyperparameters comprises: (i) eliminating outliers in the matrix to obtain a steadiness matrix P steady using a Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN); (ii) computing a steadiness parameter by dividing a maximum performance of the steadiness matrix P steady with a standard deviation associated with the steadiness matrix P steady; (iii) diversifying the steadiness parameter by forming a plurality of matrices P new representing a plurality of regions comprising the steadiness matrix P steady for analyzing new steadiness parameter corresponding to each of the plurality of matrices P new; (iv) iteratively computing new steadiness parameter based on step (ii) until a stopping criterion is satisfied, wherein the stopping criterion is a ratio of the steadiness parameter and the new steadiness parameter less than or equal to ε, wherein ε represents a deviation coefficient tending to 1; (v) selecting a new steadiness matrix P new steady corresponding to the new steadiness parameter that meets the stopping criterion; and (vi) determining a pair of optimal kernel co-efficient γ opt and optimal rejection rate hyperparameter v opt corresponding to a maximum performance element of selected new steadiness matrix P new steady using a divide-conquer algorithm; obtaining an optimal non-linear decision boundary for the OC-SVM based on the jointly optimized hyperparameters ( γ opt and v opt) for maximizing performance of binary classification, wherein the jointly optimized hyperparameters enable a learner model of the OC-SVM to form a smooth non-linear decision boundary unperturbed by outliers; and identifying cardio-vascular abnormality in Electrocardiogram and phonocardiogram signals. (i) eliminating outliers in the matrix to obtain a steadiness matrix P steady using a Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN); (ii) computing a steadiness parameter by dividing a maximum performance of the steadiness matrix P steady with a standard deviation associated with the steadiness matrix P steady; (iii) diversifying the steadiness parameter by forming a plurality of matrices P new representing a plurality of regions comprising the steadiness matrix P steady for analyzing new steadiness parameter corresponding to each of the plurality of matrices P new; (iv) iteratively computing new steadiness parameter based on step (ii) until a stopping criterion is satisfied, wherein the stopping criterion is a ratio of the steadiness parameter and the new steadiness parameter less than or equal to ε, wherein ε represents a deviation coefficient tending to 1; (v) selecting a new steadiness matrix P new steady corresponding to the new steadiness parameter that meets the stopping criterion; and (vi) determining a pair of optimal kernel co-efficient γ opt and optimal rejection rate hyperparameter v opt corresponding to a maximum performance element of selected new steadiness matrix P new steady using a divide-conquer algorithm; obtaining an optimal non-linear decision boundary for the OC-SVM based on the jointly optimized hyperparameters ( γ opt and v opt) for maximizing performance of binary classification, wherein the jointly optimized hyperparameters enable a learner model of the OC-SVM to form a smooth non-linear decision boundary unperturbed by outliers; and identifying cardio-vascular abnormality in Electrocardiogram and phonocardiogram signals. obtaining an optimal non-linear decision boundary for the OC-SVM based on the jointly optimized hyperparameters ( γ opt and v opt) for maximizing performance of binary classification, wherein the jointly optimized hyperparameters enable a learner model of the OC-SVM to form a smooth non-linear decision boundary unperturbed by outliers; and identifying cardio-vascular abnormality in Electrocardiogram and phonocardiogram signals.