Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19. © 2020 Wiley Periodicals LL

Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease

Health is so important for human beings. Thanks to the technological developments both in medicine and information technologies, the success percentages of both medical diagnosing and medical treatment systems are increasing day by day. Cancer is the most common causes of death in today’s world and is generally diagnosed at the last stages. Cancer has many types such as breast cancer, skin cancer, leukemia and etc. Diagnosis of cancer at early stages is very important for the success of medical treatments. The aim of this study was to evaluate the classification performances of some popular algorithms on the way to design an efficient computer aided breast and/or skin cancer diagnosing system to support the doctors and patients. For this purpose, same machine learning and deep learning algorithms were applied on immunotherapy dataset and breast cancer Coimbra dataset from UCI machine learning data repository. Feature selection by information gain and reliefF were applied on datasets before classification in order to increase the efficiency of classification processes. Support Vector Machines (SVM), Random Forest (RF), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms were used for classification experiments. Accuracy values are used for performance metric. According to these results, RNN has shown the best performance among the others with 92% on both datasets. This shows that deep learning algorithms especially RNN has great potential to diagnose the cancer from dataset with high success ratios. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021