Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient’s severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient’s current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.

The past few years have witnessed increased interest among researchers in cluster-based protocols for homogeneous networks because of their better scalability and higher energy efficiency than other routing protocols. Given the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster-based protocols should be compatible with these constraints in either the setup state or steady data transmission state. With focus on these constraints, we classify routing protocols according to their objectives and methods towards addressing the shortcomings of clustering process on each stage of cluster head selection, cluster formation, data aggregation and data communication. We summarize the techniques and methods used in these categories, while the weakness and strength of each protocol is pointed out in details. Furthermore, taxonomy of the protocols in each phase is given to provide a deeper understanding of current clustering approaches. Ultimately based on the existing research, a summary of the issues and solutions of the attributes and characteristics of clustering approaches and some open research areas in cluster-based routing protocols that can be further pursued are provided.

Heart disease is the leading cause of death worldwide. A Machine Learning (ML) system can detect heart disease in the early stages to mitigate mortality rates based on clinical data. However, the class imbalance and high dimensionality issues have been a persistent challenge in ML, preventing accurate predictive data analysis in many real-world applications, including heart disease detection. In this regard, this work proposes a new method to address these issues and improve the predict the presence of heart disease and patients’ survival, including supervised infinite feature selection (Inf-FS _s ) to find the most significant features and Improved Weighted Random Forest (IWRF) to predict heart disease, and Bayesian optimization to tune the new hyperparameters for IWRF. Two public datasets, including Statlog and heart disease clinical records, were used to develop and validate the proposed model. The proposed model is compared with other hybrid models to show its superiority using performance metrics like accuracy and f-measure to evaluate the models’ performance. The results have shown that the proposed Inf-FS _s -IWRF achieved better results than other models in attaining higher accuracy and F-measure on both datasets. Additionally, a comparative study has been performed to compare with previous studies, where the proposed model outperformed the others by an accuracy improvement of 2.4% and 4.6% on both datasets, respectively.