Respiratory Disease Detection Using Machine Learning for Sound Classification

Respiratory Disease Detection Using Machine Learning for Sound Classification

Authors- M Divya, Dr. C. Meenakshi

Abstract--Respiratory sounds play a meaningful part in surveying of aspiratory wellbeing and recognizing respiratory disarranges at an early arrange. The emergence of artificial intelligence (AI) has encouraged the utilization of machine learning (ML) methods so as to analyze respiratory irregularities, inclusive of conditions like asthma, pneumonia, bronchiolitis, and also chronic obstructive pulmonary disease (COPD). Conventional auscultation strategies remain important, but subjectivity, variations in clinical translation, and irregularities in sound quality often limit their adequacy. Subsequent advances in computational techniques have upgraded demonstrative precision. These advances empower mechanized discovery of various unusual lung sounds, for example, wheezes and crackles. This investigation examines if choice tree-based classification models can diagnose respiratory illnesses, reaching an impressive accuracy of 90%. The aforementioned highlights underscore the possibility of AI-powered symptomatic tools in yielding durable results and advancing respiratory medicine.

DOI: /10.61463/ijset.vol.13.issue2.366