Performance analysis of data mining algorithms for diagnosing COVID-19

BACKGROUND: An outbreak of atypical pneumonia termed COVID-19 has widely spread all over the world since the beginning of 2020. In this regard, designing a prediction system for the early detection of COVID-19 is a critical issue in mitigating virus spread. In this study, we have applied selected machine learning techniques to select the best predictive models based on their performance. MATERIALS AND METHODS: The data of 435 suspicious cases with COVID-19 which were recorded from the Imam Khomeini Hospital database between May 9, 2020 and December 20, 2020, have been taken into consideration. The Chi-square method was used to determine the most important features in diagnosing the COVID-19; eight selected data mining algorithms including multilayer perceptron (MLP), J-48, Bayesian Net (Bayes Net), logistic regression, K-star, random forest, Ada-boost, and sequential minimal optimization (SMO) were applied in data mining. Finally, the most appropriate diagnostic model for COVID-19 was obtained based on comparing the performance of the selected algorithms. RESULTS: As the result of using the Chi-square method, 21 variables were identified as the most important diagnostic criteria in COVID-19. The results of evaluating the eight selected data mining algorithms showed that the J-48 with true-positive rate = 0.85, false-positive rate = 0.173, precision = 0.85, recall = 0.85, F-score = 0.85, Matthews Correlation Coefficient = 0.68, and area under the receiver operator characteristics = 0.68, respectively, had the higher performance than the other algorithms. CONCLUSION: The results of evaluating the performance criteria showed that the J-48 can be considered as a suitable computational prediction model for diagnosing COVID-19 disease. © 2021 International Union of Crystallography. All rights reserved

Machine Learning-Based Clinical Decision Support System for Automatic Diagnosis of COVID-19 based on Clinical Data

Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm. Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented. Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9, mean specificity = 89.3, mean sensitivity = 94.2, mean F-measure = 91.6 , mean KAPA = 94.3 and mean ROC = 92.1 . Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases. © 2022 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by-nc/4.0/). Noncommercial uses of the work are permitted, provided the original work is properly cited

The impact of sofosbuvir/daclatasvir or ribavirin in patients with severe COVID-19

OBJECTIVES: Sofosbuvir and daclatasvir are direct-acting antivirals highly effective against hepatitis C virus. There is some in silico and in vitro evidence that suggests these agents may also be effective against SARS-CoV-2. This trial evaluated the effectiveness of sofosbuvir in combination with daclatasvir in treating patients with COVID-19. METHODS: Patients with a positive nasopharyngeal swab for SARS-CoV-2 on RT-PCR or bilateral multi-lobar ground-glass opacity on their chest CT and signs of severe COVID-19 were included. Subjects were divided into two arms with one arm receiving ribavirin and the other receiving sofosbuvir/daclatasvir. All participants also received the recommended national standard treatment which, at that time, was lopinavir/ritonavir and single-dose hydroxychloroquine. The primary endpoint was time from starting the medication until discharge from hospital with secondary endpoints of duration of ICU stay and mortality. RESULTS: Sixty-two subjects met the inclusion criteria, with 35 enrolled in the sofosbuvir/daclatasvir arm and 27 in the ribavirin arm. The median duration of stay was 5 days for the sofosbuvir/daclatasvir group and 9 days for the ribavirin group. The mortality in the sofosbuvir/daclatasvir group was 2/35 (6%) and 9/27 (33%) for the ribavirin group. The relative risk of death for patients treated with sofosbuvir/daclatasvir was 0.17 (95% CI 0.04-0.73, P = 0.02) and the number needed to treat for benefit was 3.6 (95% CI 2.1-12.1, P < 0.01). CONCLUSIONS: Given these encouraging initial results, and the current lack of treatments proven to decrease mortality in COVID-19, further investigation in larger-scale trials seems warranted