A real-time correlation model between lung sounds & clinical data for asthmatic patients

Lung sounds indicate the lung’s capacity to hold and breathe oxygen, which assists in evaluating its qualitative health. While clinical lung data like cough conditions, mucus levels, shortness of breath, wheezing, fatigue, age, Tiffeneau-Pinelli index, smoking conditions, etc., assist in estimating quantitative lung performance. A wide variety of system models are available for the classification of each type of condition, but limited research has been done for the correlation of these conditions in order to estimate the effect of one entity on the other. In order to perform this task, this short article proposes a novel correlation engine design is proposed in this text, which utilizes individual clinical data parameters and correlates them with lung sounds. Based on this correlation, readers will be able to estimate the dependency of these parameters for lung quality estimation. The proposed model was tested on over 60 patients, and an accuracy of 92.8% was achieved. This accuracy was better than various state-of-the-art methods, which makes the model applicable for real-time deployments. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.