Assessment of Asthma BAL Cytokines using Machine Learning Techniques

Asthma is a chronic respiratory disorder characterised by airway inflammation and constriction, leading to difficulty in breathing and recurrent attacks of wheezing, coughing, and shortness of breath. In asthma, various cytokines, including interleukins (IL-4, IL-5, and IL-13) and tumor necrosis factoralpha (TNF-alpha), have been found to be increased in the airways of individuals. These cytokines are involved in the recruitment and activation of immune cells, such as eosinophils and T-lymphocytes, which contribute to the inflammation and airway hyperresponsiveness. Dysregulation of cytokine production and signaling has been implicated in the pathogenesis of asthma and may be targeted by therapies to alleviate symptoms and improve outcomes in individuals with this disease. We propose a predictive binary and multi-class machine learning model analysis that efficiently classify the asthma and healthy control patients by detecting cytokines in bronchoalveolar lavage (BAL) fluid which achieved better F1-score than existing approaches.