Abstract
Over 600,000 new lymphoma cases and around 280,000 lymphoma-related deaths were reported in 2020. The delayed diagnosis of lymphoma has long been a problem. However, the advent of the COVID-19 pandemic, which disrupted healthcare services worldwide, may have caused more significant delays in lymphoma diagnoses. Since lymphomas can sometimes present with symptoms like COVID-19 and can affect the lungs, there is also a risk of misdiagnosis. We collected 505 lymphoma and 180 COVID-19 case reports from ScienceDirect and applied boosting methods to classify each patient as having COVID-19 or lymphoma based on the patient’s age, gender and reported symptoms. LightGBM had the highest ROC AUC (0.89), meaning it best differentiates between the two diseases. Therefore, this model can be used as a screening tool to reduce the delay in lymphoma diagnosis and improve the patients’ chances of survival.
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Pholo, M.D., Hamam, Y., Khalaf, A., Tu, C. (2023). Using Tree-Based Gradient Boosting to Distinguish Between Lymphoma and COVID-19. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-19-7663-6_43
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DOI: https://doi.org/10.1007/978-981-19-7663-6_43
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