Respiratory Disease Diagnosis Reporting System Using CNN and LSTM Machine Learning Algorithms

In order to improve the early and precise diagnosis of respiratory diseases such as asthma, pneumonia, and chronic obstructive pulmonary disease (COPD), this study suggests an advanced diagnostic method like hybrid of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. Traditional diagnostic methods rely on clinical assessments and medical imaging, which can be time-consuming and prone to human error. The CNN model gathers spatial information from respiratory audio data set, whereas the LSTM model looks at patient history and symptom development to identify temporal correlations. A patient reporting system is also integrated to collect real-time symptom data and medical history, which helps to improve diagnosis accuracy and enable a more customized evaluation. By automating analysis, generating comprehensive diagnostic results, and ensuring timely responses, this approach aims to assist healthcare providers in reducing the rate of misdiagnosis. The system uses AI-driven approaches to improve patient care, optimize healthcare decision-making, and raise diagnostic accuracy. To ensure clinical reliability, an experimental evaluation of its accuracy, sensitivity, and specificity will be conducted.