SMART DETECTION: USING SUPERVISED MACHINE LEARNING FOR RESPIRATORY DISEASES

Respiratory disease in human respiratory diseases is a leading cause of mortality worldwide, claiming nearly 900,000 lives annually. Early identification is crucial for reducing mortality rates. This review explores the innovative use of machine learning and deep learning technologies in detecting and classifying respiratory diseases, highlighting recent advancements. The review overviews machine learning approaches, and discusses various deep learning algorithms and specialized architectures. Performance evaluation includes support vector machine, logistic regression, artificial neural network, convolutional neural network, recurrent neural network, and long short-term memory, using metrics such as accuracy, precision, recall, F1-Score, and AUC. Among these, the recurrent neural network stands out with an accuracy of (83%), precision of (87%), F1-Score of (91%), and AUC of (91%). However, the artificial neural network shows a higher recall of (96%) compared to other algorithms.