<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>
Abstract:
The research aims to achieve the basis of tax justice by making a comparison between the tax accounting process according to the application of the International Financial Reporting Standard IFRS15 and the tax accounting procedures currently used in the General Authority for Taxes to determine the annual and total tax differences, to give credibility and enhance transparency in the reports and financial statements of companies Long-term contracts when prepared in accordance with the requirements of applying the international standard IFRS15 “Revenue from Contracts with Customers”, which increases the possibility of relying on them when calculating taxes. Thus, the resea
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