In late Dec.2019,a huge number of pneumonia cases caused by novel coronavirus were reported in China.2019-nCoV pandemic has influenced on millions of people's life across the world.This novel coronavirus was ident...In late Dec.2019,a huge number of pneumonia cases caused by novel coronavirus were reported in China.2019-nCoV pandemic has influenced on millions of people's life across the world.This novel coronavirus was identified to be similar with MERS and SARS.Therefore,researchers and academicians across the world still trying to find out vaccines,new drug molecules against SARS-CoV-2.The principle point of this review article is to explain the activity of favipiravir in preventing COVID-19.In view of constrained data available in the literature,we specify that favipiravir treatment,among all other anti-viral drugs,accompanied by oxygen inhalation therapy,maintaining fluid and electrolyte balance,and nutritional support may be helpful in fighting COVID-19.Researches were done on already approved existing anti-viral drugs for treating ebola virus,influenza virus infection and many such anti-viral agents like favipiravir,ritonavir,remdesivir,ribavirin,oseltamivir shows promising results in preventing COVID-19 infection and their clinical trials are currently undergoing in order to discover proper treatment of COVID-19.Among the aforementioned drug candidates,a broad-spectrum RNA polymerase inhibitor favipiravir,which demonstrated a promising tolerance profile and anti-viral efficacy in patients having COVID-19 manifestations.展开更多
Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing va...Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing values in medical datasets.Imputation methods are used to predict these missing values.In this paper,a new imputation method called hybrid imputation optimized by the classifier(HIOC)is proposed to predict missing values efficiently.Design/methodology/approach-The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations(MICE),K nearest neighbor(KNN),mean and mode imputation methods in an optimum way.Performance of HIOC has been compared to MICE,KNN,and mean and mode methods.Four classifiers support vector machine(SVM),naive Bayes(NB),random forest(RF)and decision tree(DT)have been used to evaluate the performance of imputation methods.Findings-The results show that HIOC performed efficiently even with a high rate of missing values.It had reduced root mean square error(RMSE)up to 17.32%in the heart disease dataset and 34.73%in the breast cancer dataset.Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases.It increased classification accuracy up to 18.61%in the heart disease dataset and 6.20%in the breast cancer dataset.Originality/value-The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.展开更多
文摘In late Dec.2019,a huge number of pneumonia cases caused by novel coronavirus were reported in China.2019-nCoV pandemic has influenced on millions of people's life across the world.This novel coronavirus was identified to be similar with MERS and SARS.Therefore,researchers and academicians across the world still trying to find out vaccines,new drug molecules against SARS-CoV-2.The principle point of this review article is to explain the activity of favipiravir in preventing COVID-19.In view of constrained data available in the literature,we specify that favipiravir treatment,among all other anti-viral drugs,accompanied by oxygen inhalation therapy,maintaining fluid and electrolyte balance,and nutritional support may be helpful in fighting COVID-19.Researches were done on already approved existing anti-viral drugs for treating ebola virus,influenza virus infection and many such anti-viral agents like favipiravir,ritonavir,remdesivir,ribavirin,oseltamivir shows promising results in preventing COVID-19 infection and their clinical trials are currently undergoing in order to discover proper treatment of COVID-19.Among the aforementioned drug candidates,a broad-spectrum RNA polymerase inhibitor favipiravir,which demonstrated a promising tolerance profile and anti-viral efficacy in patients having COVID-19 manifestations.
文摘Purpose-Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases.However,the performance of these systems is adversely affected by the missing values in medical datasets.Imputation methods are used to predict these missing values.In this paper,a new imputation method called hybrid imputation optimized by the classifier(HIOC)is proposed to predict missing values efficiently.Design/methodology/approach-The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations(MICE),K nearest neighbor(KNN),mean and mode imputation methods in an optimum way.Performance of HIOC has been compared to MICE,KNN,and mean and mode methods.Four classifiers support vector machine(SVM),naive Bayes(NB),random forest(RF)and decision tree(DT)have been used to evaluate the performance of imputation methods.Findings-The results show that HIOC performed efficiently even with a high rate of missing values.It had reduced root mean square error(RMSE)up to 17.32%in the heart disease dataset and 34.73%in the breast cancer dataset.Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases.It increased classification accuracy up to 18.61%in the heart disease dataset and 6.20%in the breast cancer dataset.Originality/value-The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.
