Objective: While the coronavirus persists marginally for ninety-five percent of the infected case count, the remaining five percent have been placed in a critical or vital condition. This study investigates to design an intelligent model that predicts the disease severity level by modeling the relationships between the severity of COVID-19 infection and the various demographic/clinical characteristics of individuals. Material and Methods: A public dataset of a cross-sectional study included the demographic and symptomatological characteristics of 223 COVID-19 patients. The dataset was randomly divided into training (75%) and testing (25%) datasets. During training, the class imbalance problem was solved, and the related factors with the COVID-19 severity were selected using the evolutionary method supported by a genetic algorithm. Neural Network (NN), Support Vector Machine (SVM), QUEST algorithms together with confidence weighted voting, voting, and highest confidence wins strategies (HCWS) were constructed, and the predictive power of models was evaluated by performance metrics. Results: Of the individual models, the NN model outperformed SVM and QUEST algorithms based on the performance metrics in the training and testing datasets. However, ensemble approaches gave better predictions as compared to the individual models regarding all the evaluation metrics. Conclusions: The proposed voting ensemble model outperforms other ensemble and individual machine learning approaches for the severity prediction of COVID-19 disease. The proposed ensemble learning model can be integrated into web or mobile applications in classifying the severity of COVID-19 for clinical decision support.

Purpose: COVID-19 pandemic has created a serious psychological impact worldwide since it has been declared. This study aims to investigate the level of psychological impacts of COVID-19 pandemic on Turkish population and to determine related factors. Methods: The study was carried out by using an online questionnaire using the virtual snowball sampling method. The sociodemographic data were collected on the following subjects: Participants’ experience on any signs of infection within the last month, the history of COVID-19 contact-treatment-quarantine, level of compliance with precautionary measures, the sources of information and level of knowledge about the pandemic process and their belief levels on the knowledge they acquire. Besides, questions that take place in the depression, anxiety, stress scale (DASS-21), and impact of events scale (IESR) were asked. Results: Of the 3549 participants, anxiety was found in 15.8%, depression in 22.6%, stress in 12.9%, and psychological trauma in 20.29%. Female gender, young age, higher education level, being single, high monthly income, presence of psychiatric illness, a high number of people living together, having any signs of infection, and contact history with COVID-19 infected person or contaminated object are identified as risk factors that may increase psychological impact. Compliance with the rules was found to reduce the risk of psychological response. Conclusions: During the pandemic, reducing the spread of the virus and knowing the risk factors in protecting the mental health of individuals will be guided in determining the measures to be followed and the policies to be followed.