Comparing machine learning algorithms to predict COVID19 mortality using a dataset with chest computed tomography severity score information

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Comparing Machine Learning Algorithms to Predict COVID-19 Mortality Using Chest Computed Tomography Severity Score Data

Researchers at Ayatollah Talleghani Hospital in Abadan, Iran have conducted a study to compare the effectiveness of various machine learning algorithms in predicting the mortality rate of COVID-19 patients. The study, published in Scientific Reports, analyzed data from a hospital-based registry database containing information on 815 COVID-19 patients between February and December 2020.

The dataset included patient demographics, clinical features, history of personal diseases, laboratory results, and chest computed tomography (CT) severity scores. The severity of pulmonary involvement in CT images was assessed using a scoring system. Two radiologists reviewed the CT images, with any disagreements being resolved by an experienced attending radiologist.

To ensure data quality, a thorough pre-processing step was performed, which involved excluding records with more than 70% missing data and imputing missing values for continuous and discrete variables. Noisy and abnormal values were also addressed by consulting an expert panel.

The study focused on positive RT-PCR COVID-19 cases and excluded negative test results, unknown dispositions, and patients under 18 years old. After applying these criteria, the final sample size consisted of 707 cases in the survival group and 108 cases in the death group.

To address the issue of imbalanced data, the researchers used the synthetic minority over-sampling technique (SMOTE), a method that creates synthetic samples of the minority class (deceased patients) using instances of the minority class and their nearest neighbors. This technique helps to balance the dataset and prevents biased results.

To determine the most important variables for mortality prediction, the researchers employed XGBoost, random forest, and chi-squared tests. These tests identified variables such as CT severity scores, white blood cell count, and serum creatinine as strong predictors of mortality.

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The study utilized eight machine learning algorithms, including decision trees, support vector machines, and logistic regression, to develop predictive models for COVID-19 mortality. The performance of these models was evaluated using metrics such as accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC).

The researchers obtained ethical approval for the study from the Abadan University of Medical Sciences and ensured patient privacy and confidentiality throughout the research process. Informed consent was obtained from all subjects or their legal guardians.

The findings of this study highlight the potential of machine learning algorithms in accurately predicting the mortality rate of COVID-19 patients. By leveraging the power of artificial intelligence and analyzing key variables, healthcare professionals can gain valuable insights that aid in making informed decisions and providing timely interventions. This research contributes to the growing body of knowledge on using machine learning in healthcare, particularly in the context of the COVID-19 pandemic.

Frequently Asked Questions (FAQs) Related to the Above News

What was the objective of the study conducted by researchers at Ayatollah Talleghani Hospital?

The objective of the study was to compare the effectiveness of various machine learning algorithms in predicting the mortality rate of COVID-19 patients.

What data was analyzed in the study?

The study analyzed data from a hospital-based registry database, including patient demographics, clinical features, personal disease history, laboratory results, and chest computed tomography (CT) severity scores.

How were CT severity scores assessed?

Two radiologists reviewed CT images and assessed the severity of pulmonary involvement using a scoring system. Any disagreements were resolved by an experienced attending radiologist.

How was data quality ensured in the study?

Data quality was ensured through a thorough pre-processing step that involved excluding records with more than 70% missing data, imputing missing values, and addressing noisy and abnormal values with the help of an expert panel.

Which patients were included in the study?

The study focused on positive RT-PCR COVID-19 cases and excluded negative test results, unknown dispositions, and patients under 18 years old.

How was imbalanced data addressed in the study?

The researchers used the synthetic minority over-sampling technique (SMOTE) to create synthetic samples of the minority class (deceased patients) and balance the dataset, preventing biased results.

Which variables were identified as strong predictors of mortality?

Variables such as CT severity scores, white blood cell count, and serum creatinine were identified as strong predictors of mortality.

Which machine learning algorithms were used in developing predictive models?

Eight machine learning algorithms, including decision trees, support vector machines, and logistic regression, were utilized in developing predictive models for COVID-19 mortality.

What metrics were used to evaluate the performance of the predictive models?

The performance of the predictive models was evaluated using metrics such as accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC).

How was ethical approval and patient privacy ensured in the study?

The researchers obtained ethical approval for the study, ensured patient privacy and confidentiality, and obtained informed consent from all subjects or their legal guardians.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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