Abstract:
Over 200 million people worldwide suffer from asthma, a common chronic respiratory condition that claims about 450,000 lives each year. The application of machine learning to aid in decision-making in the healthcare industry is growing. It works especially well for activities like diagnosis, prediction, and clinical insight in the management of asthma. By seeing trends, predicting future asthma episodes, and supporting individualized care regimens, these technologies assist enhance treatment results. By adjusting hyperparameters and implementing effective models, this work aims to enhance the prediction of asthma disease through machine learning algorithms. Many machine learning models, including XGBoost, Random Forest, K-Nearest Neighbors (kNN), Decision Tree, and Logistic Regression, were trained and evaluated using a dataset of 316801 unbalanced samples from Kaggle, enhanced by 285 survey replies. With an accuracy of 0.7012 and superior performance over other models in terms of precision, recall, and F1-score, XGBoost stood out among the rest. Model dependability was increased by applying SMOTETomek to overcome data imbalance. In order to improve classification accuracy, the study also investigates the effects of several feature selection strategies and hyperparameter tweaking. According to the results, using machine learning models that have been tuned can greatly enhance asthma prediction, enabling early diagnosis and treatment. The effort will concentrate on creating a real-time forecasting system for clinical applications, and taking genetic and environmental factors into account. This study advances AI-powered medical solutions for the diagnosis of asthma. According to this study, machine learning (ML) has the potential to be a very effective technique for forecasting asthma flare-ups. Nevertheless, these models' approaches varied widely. Future research should concentrate on enhancing the models' practicability and generalizability in order to promote their use in clinical practice
