Eleven Routine Clinical Features Predict COVID-19 Severity Uncovered by Machine Learning of Longitudinal Measurements

Zhou, Kai; Sun, Yaoting; Li, Lü; Zang, Zelin; Wang, Jing; Li, Jun; Liang, Junbo; Zhang, Fangfei; Zhang, Qiushi; Ge, Weigang; Chen, Hao; Sun, Xindong; Liang, Yue; Wu, Xiaomai; Shen, Bo; Xu, Jiaqin; Zhu, Hongying; Chen, Shiyong; Yang, Hai; Huang, Shigao; Peng, Minfei; Lv, Dongqing; Zhang, Chao; Zhao, Haihong; Hong, Luxiao; Zhou, Zhehan; Chen, Haixiao; Dong, Xuejun; Tu, Chunyu; Li, Minghui; Zhu, Yi; Chen, Baofu; Li, Stan Z.; Guo, Tiannan

doi:10.2139/ssrn.3669140

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance with this purpose, a support vector machine model was constructed with a genetic algorithm for feature selection and achieved an accuracy of over 94% for COVID-19 severity prediction. The authors report that the proposed model includes 11 routine clinical features commonly available during COVID-19 management, which may predict the severity and guide the treatment of COVID-19 patients (26). In another recently published study, RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities were performed on 219 molecular features with high significance to COVID-19 status and severity.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Severity Prediction in SARS-CoV-2 RNA-Positive Patients by Different Ensemble Learning Strategies

GÜLDOĞAN,

KIVRAK,

Colak

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%