Cross-Validation of a Global Machine Learning Model to Predict COVID-19 Mortality

Minhas, Hersimren; Malik, Adrienne; Kurtz, Daniel; Fatiwala, Z.-U.-A.; Ahmed, Farah; Irfan, Furqan B.; Lee, S.; Esber, Zahia

doi:10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a3171

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K-fold Cross Validation1

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2022

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“…Each time the data model is trained, one of the folds is used as the validation set, and the other folds are used as the training set [46,47], and the performance value si of the trained data model is obtained after verification. In this way, the training iteration of the data model is performed k times [48], and each fold will be used as a validation set once and k-1 times as a training set [49]. Finally, the performance evaluation result of the data model is the average value s of the k performance values, as shown in formula (6).…”

Section: K-fold Cross Validationmentioning

confidence: 99%

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Han

Wang

2022

EAI Endorsed Trans e-Learn

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In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.

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Section: K-fold Cross Validationmentioning

confidence: 99%