Respiratory Disease Classification Lungs Sounds Using Machine Learning

Abstract: Globally, respiratory conditions like asthma, pneumonia, and chronic obstructive pulmonary disease (COPD) represent a serious threat to public health. Improvingpatientoutcomesandloweringhealthcarecosts depend on early and precise diagnosis. Despite being widely used, traditional stethoscope auscultation is constrainedbysubjectivityandinterobservervariability. This study investigates the use of machine learning methods to categorize respiratory conditions from recordings of lung sounds. To improve the quality of auscultation signals, preprocessing techniques like segmentation and noise filtering are used. To capture the temporalandspectralcharacteristicsofthelungsounds,a number of featuresareextracted,suchasspectralentropy, zero-crossing rate, and Mel Frequency Cepstral Coefficients (MFCCs). Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNNs) are example of supervised learning algorithms that are trained.