Clinical outcome prediction in pediatric respiratory infections using hybrid feature selection and a genetic algorithm-optimized machine learning.

Respiratory ailments constitute various pathological conditions affecting the respiratory system, including the airways, pulmonary tissues, and associated structures. When these conditions are left untreated or inadequately managed, they can result in long-term complications, diminished life quality, and higher death rates. To alleviate the strain of respiratory illnesses and promote a more robust population, it is crucial to focus on raising public awareness, facilitating early detection, implementing preventive strategies like immunization, and furthering medical advancements in treatment options. The study presents a comprehensive Machine Learning (ML) method to improve the investigation and classification of respiratory datasets. The technique applies data preprocessing, augmentation, feature selection, genetic algorithms, and ensemble learning techniques on a "Respiratory dataset" and achieves high predicted accuracy while maintaining interpretability. The Synthetic Minority Oversampling Technique (SMOTE) is used to address data imbalance and ensure proper representation of minority class samples. The feature selection module uses various strategies to find relevant characteristics and reduce dimensionality. Machine learning algorithms that are apt for the dataset are employed for predicting the target variable; their performance is measured and analyzed thoroughly. By using Genetic algorithms, Random Forest, XGBoost, and Gradient Boosting are selected as optimal models. The ensemble learning framework combines the 3 optimal models and creates a strong classification system to predict "target variable : Clinical Progression" output. The performance measures of the proposed model achieved an overall accuracy of 95.02% when compared with the existing works and can be applied in healthcare analytics.