Abstract

Abstract: Asthma is a chronic respiratory condition that affects millions of people worldwide, leading to significant healthcare costs and a reduced quality of life for affected individuals. Timely and accurate detection of asthma exacerbations is crucial for effective management and intervention. Traditional methods of asthma diagnosis rely heavily on clinical assessments, which may not always provide real-time, objective data for prompt action. The aim of this project is to develop an AI-based asthma detection system that leverages the analysis of respiratory sound patterns. Current diagnostic tools often lack the ability to capture subtle changes in respiratory sounds that could indicate the onset or worsening of asthma symptoms. By employing advanced machine learning algorithms, this system aims to identify distinctive patterns and anomalies in respiratory sounds associated with asthma, enabling early detection and intervention.

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