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This retrospective study reviewed the clinical and radiological evolution of 265 adult patients with COVID-19 attended at our center between March 2020 and April 2020. We recorded data related to patients’ comorbidities, hospital stay, and clinical worsening (admission to the ICU, intubation, https://forexhero.info/ and death). We used three scoring systems taking into consideration 6 or 8 lung fields (designated 6  A, 6 B, and 8) to quantify lung involvement in each patient’s initial abnormal chest X-ray and to classify its severity as mild, moderate, or severe, and we compared these three systems.

As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI). In this article, I will explain the key differences between regression and classification supervised machine learning algorithms. It is important to understand the differences before an appropriate machine learning algorithm can be chosen. None of the methods tested outperformed the others in terms of discriminative ability. We found that the CART model was much easier to use and interpret, because the decision rules generated could be applied without the need for mathematical calculations.

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We also recorded the presence of alveolar opacities and linear opacities (fundamentally linear atelectasis) in the first chest X-ray with pathologic findings. No significant difference was found in the predictive ability of the three classification systems proposed. We aimed to analyze the relationship between the diferencia entre regresion y clasificacion initial chest X-ray findings in patients with severe acute respiratory syndrome due to infection with SARS-CoV-2 and eventual clinical worsening and to compare three systems of quantifying these findings. To provide an overview of decision trees based on CART (Classification and Regression Trees) methodology.