Title: Using Machine Learning Techniques to Predict Type 2 Asthma Disease (Comprehensive Journal of Science, 2025)
Asthma, a chronic inflammatory disease of the airways, affects millions globally. Among itsphenotypes, Type 2 asthma is characterized by eosinophilic inflammation and responds differentlyto treatment compared to non-Type 2 variants. Accurate early diagnosis of this subtype is critical toensuring appropriate therapy and reducing long-term complications. This research investigates theapplication of machine learning techniques to predict the presence of Type 2 asthma using astructured and anonymized clinical dataset obtained from a trusted health registry. The studyleverages a real dataset from the University of Washington, which includes 2,392 patient recordswith a wide range of features, including demographic information, lifestyle habits, environmentalexposures, clinical symptoms, and spirometry results. Various supervised machine learningalgorithms, including Logistic Regression, Support Vector Machines, Decision Trees, RandomForest, and XGBoost, were trained and evaluated. Results show that ensemble methods outperformbaseline models, highlighting the promise of machine learning in improving diagnostic precision inasthma care. The findings demonstrate that the employed methods can predict and provide apreliminary diagnosis of asthma based on disease-related symptoms, thereby assisting physicians indelivering better and faster care to patients before their condition deteriorates
Keywords: artificial intelligence (AI), asthma, , chronic disease, machine learning, asthma, XGBoost