International Journal of Science, Engineering and TechnologyAuthors: Shaik Rahena, Associate Professor Mrs.M.Radhika
Abstract: There is a substantial clinical and health care burden associated with bronchiectasis, a chronic respiratory disorder characterised by frequent exacerbations, persistent symptoms, and frequent hospitalisations. Conventional treatment techniques rely on clinician opinion and assessment based on guidelines, which may not adequately reflect the unique risk heterogeneity of each patient. This research presents a paradigm for bronchiectasis risk classification and intervention assistance using machine learning and routinely gathered clinical data. The purpose of analysing a structured dataset that includes demographic, clinical, etiological, lifestyle, and treatment-related variables is to divide patients into groups with varying degrees of risk. Stratified cross validation testing is used for a variety of machine learning models, including feedforward neural networks, Random Forest, XGBoost, and Logistic Regression. Based on the data, it seems that the baseline clinical characteristics are generally linearly separable, as Logistic Regression provides the greatest prediction performance. Ensemble and neural models work well together and compete well in terms of interpretability and decision-support. Applying explainable AI with SHAP helps to make sense of model predictions and isolate key risk variables. Further patient variables uncovered by unsupervised clustering included exacerbation load and disease duration. The suggested approach combines phenotyping, predictability, and explainability to help with individual-level respiratory health management and data utilisation for treatment decision-making in bronchiectasis.
