BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Rahman, Tawsifur; Chowdhury, Muhammad E. H.; Khandakar, Amith; Mahbub, Zaid Bin; Hossain, Md Sakib Abrar; Alhatou, Abraham; Abdalla, Eynas; Muthiyal, Sreekumar; Islam, Khandaker Farzana; Kashem, Saad Bin Abul; Khan, Muhammad Salman; Zughaier, Susu M.; Hossain, Maqsud

doi:10.1007/s00521-023-08606-w

Cited by 16 publications

(4 citation statements)

References 84 publications

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“…Two datasets were used, and the accuracies obtained for them were 92% and 90%, respectively. Chest X-ray images and clinical data were used to predict COVID-19 severity in another research 57 . 930 COVID-19 patients from Italy were considered for this research, and the stacking classifier achieved an accuracy of 89.03%.…”

Section: Discussionmentioning

confidence: 99%

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Chadaga,

Prabhu,

Sampathila

et al. 2024

Sci Rep

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The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.

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Section: Discussionmentioning

confidence: 99%

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Chadaga,

Prabhu,

Sampathila

et al. 2024

Sci Rep

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“…Ensemble learning, which combines the predictive abilities of two or more base learner models to reduce bias and variance, thereby improving overall prediction performance, has seen recent application in analyzing COVID-19 data. However, determining the optimal combination of models for optimal performance remains challenging, with most researchers manually constructing stacking ensemble models to predict COVID-19's clinical severity [32][33][34][35]. Additionally, some studies opted for simplified severity classifications related only to survival and death, rather than attempting to classify a diverse range of clinical severities [36,37].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients

Kim,

Ju,

Seok

et al. 2024

Applied Sciences

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During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions.

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“…Finally, the authors investigated different image enhancement techniques for each of the created folds. Figure 4 represents techniques for fold creation and augmentation, which performed following the literature in [37][38][39]. Image enhancement techniques are demonstrated in Figure 5.…”

Section: Dataset Preprocessingmentioning

confidence: 99%

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

Hossain,

Gul,

Chowdhury

et al. 2023

Sensors

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The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.

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BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Cited by 16 publications

References 84 publications

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

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