Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods

The ESC ACCA EAPCI EORP acute coronary syndrome ST-elevation myocardial infarction registry

Aims: The Acute Cardiac Care Association (ACCA)-European Association of Percutaneous Coronary Intervention (EAPCI) Registry on ST-elevation myocardial infarction (STEMI) of the EurObservational programme (EORP) of the European Society of Cardiology (ESC) registry aimed to determine the current state of the use of reperfusion therapy in ESC member and ESC affiliated countries and the adherence to ESC STEMI guidelines in patients with STEMI. Methods and results: Between 1 January 2015 and 31 March 2018, a total of 11 462 patients admitted with an initial diagnosis of STEMI according to the 2012 ESC STEMI guidelines were enrolled. Individual patient data were collected across 196 centres and 29 countries. Among the centres, there were 136 percutaneous coronary intervention centres and 91 with cardiac surgery on-site. The majority of centres (129/196) were part of a STEMI network. The main objective of this study was to describe the demographic, clinical, and angiographic characteristics of patients with STEMI. Other objectives include to assess management patterns and in particular the current use of reperfusion therapies and to evaluate how recommendations of most recent STEMI European guidelines regarding reperfusion therapies and adjunctive pharmacological and non-pharmacological treatments are adopted in clinical practice and how their application can impact on patients' outcomes. Patients will be followed for 1 year after admission. Conclusion: The ESC ACCA-EAPCI EORP ACS STEMI registry is an international registry of care and outcomes of patients hospitalized with STEMI. It will provide insights into the contemporary patient profile, management patterns, and 1-year outcome of patients with STEMI