Short-Term Prognosis of Asthma Flare-ups: A Predictive Model

In order to forecast asthma exacerbations, this work makes use of machine learning algorithms, particularly Support Vector Machine (SVM) and Naive Bayes (NB). Numerous patient data, including symptoms, medical history, and demographics, are included in the collection. The data is separated into training and testing sets after thorough pre-processing, and pertinent features are carefully chosen. The SVM and NB models are assessed on the testing set using metrics such as F1 score, accuracy, precision, and recall. On the training set, the models are trained. Throughout the study, ethical issues are given first priority, and the models that are produced have the potential to improve asthma management in primary care settings and influence clinical decision-making.