(untitled) (Comprehensive Journal of Science, 2025)
Asthma, a chronic inflammatory disease of the airways, affects millions globally. Among its phenotypes, Type 2 asthma is characterized by eosinophilic inflammation and responds differently to treatment compared to non-Type 2 variants. Accurate early diagnosis of this subtype is critical to ensuring appropriate therapy and reducing long-term complications. This research investigates the application of machine learning techniques to predict the presence of Type 2 asthma using a structured and anonymized clinical dataset obtained from a trusted health registry. The study leverages a real dataset from the University of Washington, which includes 2,392 patient records with a wide range of features, including demographic information, lifestyle habits, environmental exposures, clinical symptoms, and spirometry results. Various supervised machine learning algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, and XGBoost, were trained and evaluated. Results show that ensemble methods outperform baseline models, highlighting the promise of machine learning in improving diagnostic precision in asthma care. The findings demonstrate that the employed methods can predict and provide a preliminary diagnosis of asthma based on disease-related symptoms, thereby assisting physicians in delivering better and faster care to patients before their condition deteriorates