A novel Time series Clustering and Analysis Method for ECG (Electro Cardiogram) heart-beat Analysis is proposed using K-medoids Clustering with Dynamic Time Warping (DTW) distance. The main objective of this paper is to identify the abnormalities in ECG heart beats through Clustering and Validation by using QRS complexes of ECG heart-beats. The ECG data obtained from MIT-BIH Arrhythmia Database, is used for experimentation. The 5 types of classes in ECG heart beats, used in this study are Normal (N), Left bundle branch blocks (LBBB), Right bundle branch blocks (RBBB), Premature ventricular contraction (PVC), Atrial premature contraction (APC).

An ElectroCardiogram (ECG) inter-patient heartbeat time series classification method by a hierarchical system of based on support vector machine and Decision rule, using full heart-beat time series by alignment of R-peaks of all beats, is proposed. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative analysis confirms the viability and superiority of the proposed approach in terms of Total classification accuracy (TCA). Proposed system achieved Sensitivities of 98.7%, 85.9%, 88.8%, 58.3%, PPV% of 98.53%, 82.2%, 89.9%, 85.6% for N, S, V and F classes respectively and a TCA of 97.3%.

Early detection of cardiovascular diseases can prevent the premature deaths caused by abnormal heartbeat problems. Application of unsupervised classification by Extreme learning machine is addressed for ElectroCardiogram (ECG) heart-beat time series clustering by a hybrid of Extreme learning machine and Decision rule using full heart-beat time series by alignment of R-peaks of all beats is proposed in this work. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Because of the large patient specific characteristics in ECG heartbeat morphology across individuals, the supervised methods trained on some ECG dataset may decrease performance on other datasets. In addition, these techniques require a considerable amount of known and labelled heartbeats which are not feasible in long-term ECG monitoring. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative analysis confirms the viability and superiority of the proposed approach in terms of Total classification accuracy (TCA). Proposed system achieved Sensitivities of 98.13%, 82.25%, 76.49% and 52.20%, PPV of 98.13%, 64.46%, 95.47%, 46.54% for N, S, V, and F classes respectively and TCA of 95.75%.

Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes. Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency. Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities. This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN). Patient data were stratified by age group and gender to assess the model’s performance across diverse demographic profiles. Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy. Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%. These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management.

Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.