ST segment, T wave changes, QT interval changes, and QTc dispersion are among the parameters used to diagnose ischemic heart disease. The increase in the QT dispersion can be caused by myocardial ischemia, among other heart diseases, whereas cardiac diseases such as coronary artery disease (CAD) can be diagnosed by observing an abnormally high QTc dispersion. This study aimed to evaluate the variations in the QTc dispersion (depolarization and repolarization) of surface electrocardiography as a result of percutaneous coronary intervention (PCI) in patients with chronic total occlusion. This study took place in the Iraqi Center for Heart Disease from October 2020 to February 2021. 110 patients who suffered from chronic occlusion of the coronary artery and underwent PCI revascularization were examined. Twelve-lead electrocardiograms were recorded at the time of admission (12 hours before intervention) and more than one hour after the intervention. The measured ECG parameters included corrected QT interval durations and corrected QT dispersion in both pre and post-PCI electrocardiograms, and their values were compared. The average corrected QT interval and QTC dispersion changed significantly before and after the percutaneous coronary intervention. Performing percutaneous coronary intervention on patients who suffer from coronary artery total occlusion shows a major reduction in the corrected QT dispersion.
In this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
In this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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