<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.</span>
Autorías: Ismael Saleem Abed, Imad Kadhim Khlaif, Salah Mahmood Salman. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 5, 2022. Artículo de Revista en Dialnet.
In the city of Hebron, small business industrial organizations face a major challenge related to its ability to reach, attract and sustain a sufficient number of customers in order to ensure its continuity and sustainability. The research problem is summarized in an attempt to reveal how the e-marketing could improve and support the marketing effectiveness of small business industrial organizations in the city of Hebron/Palestine. The importance of this research stems from the fact that it addresses a new knowledge branch of the field of marketing, which is electronic marketing for small business organizations, and the fact that the research highlights appropriate marketing solutions for these organizations in light of the Intern
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Be standard Malcolm Baldrige Award of several elements: - leadership , strategic planning , foc
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