Background: Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases. Results: It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases. Conclusion: Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.

Wireless Sensor Networks (WSNs) achieve much attention from various domains because of its easy maintenance, self-configuration, and scalability characteristics. It is comprised of small-sized sensors that interact with the Internet of Things (IoT) for observing and recording the physical conditions. The sensor nodes are autonomous and construct inter-communication topology with each other in an ad-hoc manner. However, the main restrictions of sensor nodes are their finite resources for energy management, data storage, transmission, and processing power. Different solutions have been addressed by researchers to overcome network performance due to bounded limitations of such battery-powered nodes, however, equalize the energy consumption and maintain the network throughput are the main research problems. Furthermore, due to the compromised nodes, the data is more prone to security vulnerabilities. Therefore, their security over the unpredictable network is other research concerns. Thus, the aim of this research article to propose a secure and energy-aware heuristic-based routing (SEHR) protocol for WSN to detect and prevent compromising data with efficient performance. Firstly, the proposed protocol makes use of an artificial intelligence-based heuristic analysis to accomplish a reliable, and intellectual learning scheme. Secondly, it protects the transmissions against adversary groups to attain security with the least complexity. Moreover, the route maintenance strategy is also achieved by using traffic exploration to reduce link failures and network dis-connectivity. The simulation results demonstrated the SEHR protocol improves the efficacy for network throughput by an average of 18%, packet drop ratio by 42%, end-to-end delay by 26%, energy consumption by 36%, faulty routes by 38%, network overhead by 44%, and computational overhead by 43% in dynamic scenarios as compared to existing work.

Breast Cancer (BC) has become a critical illness with a high mortality rate during the previous decade. It is considered the women’s most common cancer. In this paper, we propose two optimum automated BC classification approaches based on a hybridization of the Whale Optimization Algorithm (WOA) and Dragonfly Algorithm (DA), with Radial Basis Function Kernel Support Vector Machines (RBF-SVM), to increase the accuracy of BC classification (CA) by determining the optimum SVM parameters. The effectiveness of the proposed WOA-SVM and DA-SVM algorithms is tested on the Wisconsin Diagnosis Breast Cancer (WDBC) databases and the Wisconsin Breast Cancer Database (WBCD). Various metric parameters such as CA, confusion matrix, the area under the ROC curve (AUC), sensitivity, and specificity are utilized to assess and consider the effectiveness of the proposed approaches. The results are compared not only to the most common optimizers, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), that are used for training SVM and artificial neural networks (ANN) classifiers, but also to other classification models. The WOA-SVM and DA-SVM are also explored for feature selection, and their findings are compared to the offered models. According to the experimental results, the proposed WOA-SVM method outperforms previous classification approaches on the WBCD dataset. On the WDBC dataset, however, the proposed DA-SVM algorithm outperforms the previous classification algorithms. Using typical datasets’ partition, the resultant CA is as high as 99.65% and 100% for WDBC and WBCD, respectively. However, using a 10-fold cross-validation datasets’ partition, the mean resultant CA are 97.89% and 99.27%, respectively.

A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository https://github.com/shamiktiwari/ PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.