Application and utility of boosting machine learning model based on laboratory test in the differential diagnosis of non-COVID-19 pneumonia and COVID-19

Baik, Seung Min; Hong, Kyung Sook; Park, Dong Jin

doi:10.1016/j.clinbiochem.2023.05.003

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…Liver biomarkers such as D. Bilirubin, T. Bilirubin, AST, ALT and ALP were elevated in the non-COVID-19 group [65]. [20] used boosting models to conduct differential diagnosis among COVID-19 and other patients. Model explainability was achieved using SHAP.…”

Section: Discussionmentioning

confidence: 99%

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An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas

Chadaga,

Prabhu,

Sampathila

et al. 2024

IEEE Access

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It is tough to clinically differentiate between mild COVID-19 and other similar influenzas due to their comparable transmission traits and symptoms. The Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test is utilized regularly to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) despite being prone to false-negative results. In recent years, intelligent support systems have been developed for patient triage and disease diagnosis. Thus, this research utilizes machine learning to diagnose COVID-19 from routine biomarkers. Twelve feature selection techniques, which include nature-inspired techniques, have been compared to extract the essential features. Multiple classifiers, including stacking, voting and deep learning, are trained to predict the patient diagnosis. The maximum accuracy obtained by the classifiers was 95% in this retrospective study. The diagnostic predictions were further interpreted using five explainable artificial intelligence methods. Biomarkers such as albumin, protein, eosinophil and total white blood cells were crucial. Thus, automated diagnostic systems can be supportive in the accurate and timely detection of COVID-19 and similar influenza infections.

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Section: Discussionmentioning

confidence: 99%

“…The automated model performed well with an accuracy of 98.4%. Baik et al [20] used a boosting algorithm to classify COVID-19 and non-COVID-19 illnesses. The xgboost classifier obtained a maximum AUC of 0.99.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas

Chadaga,

Prabhu,

Sampathila

et al. 2024

IEEE Access

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“…These methodologies are systematically explored using relevant input datasets, yielding valuable insights crucial for informed healthcare decision-making. Consequently, they serve as the fundamental building blocks for the development of robust pneumonia detection and diagnosis strategies [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Concatenated Modified LeNet Approach for Classifying Pneumonia Images

Jaganathan,

Balsubramaniam,

Sureshkumar

et al. 2024

JPM

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Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates a revised Rectified Linear Unit (ReLU) activation function. This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. Batch normalization addresses internal covariate shift, a phenomenon where the distribution of activations within a network alter during training. These modifications help to prevent overfitting and decrease computational time. A comprehensive dataset is used to evaluate the model’s performance, and the model is benchmarked against relevant deep-learning models. The results demonstrate a high recognition rate, with an accuracy of 96% in pneumonia image recognition. This research suggests that the Concatenated Modified LeNet classifier has the potential to be a highly useful tool for medical professionals in the diagnosis of pneumonia. By offering accurate and efficient image classification, our model could contribute to improved treatment decisions and patient outcomes.

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“…Their application spans across multiple areas such as diagnosis, prognosis, therapy, and public health management. [6][7][8][9][10] The inclusion of a literature review holds significant value within the realm of academic research as it serves as a crucial means of gathering pertinent information pertaining to a particular subject matter. [11] Systematic reviews offer a rigorous, empirical, and transparent approach to accessing a large number of published studies.…”

Section: Introductionmentioning

confidence: 99%

“…Their application spans across multiple areas such as diagnosis, prognosis, therapy, and public health management. [6–10]…”

Section: Introductionmentioning

confidence: 99%

COVID-19 studies involving machine learning methods: A bibliometric study

Baygül Eden,

Bakir Kayi,

Erdem

et al. 2023

Medicine

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Background: Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis. Methods: A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included “machine learning,” “artificial intelligence,” and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19. Results: In the WoS Core, the average citation count was 13.6 ± 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, “Covid-19” appeared 1983 times, followed by “machine learning” and “deep learning.” The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling. Conclusion: This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.

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Application and utility of boosting machine learning model based on laboratory test in the differential diagnosis of non-COVID-19 pneumonia and COVID-19

Cited by 5 publications

References 38 publications

An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas

An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas

Concatenated Modified LeNet Approach for Classifying Pneumonia Images

COVID-19 studies involving machine learning methods: A bibliometric study

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