Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

“…In a recent study, 39 features of respiratory sound were integrated with three lung function features derived from 30 COPD patients and 25 healthy subjects, and five ML classifiers were used to categorize normal individuals and COPD patients. Support vector machine and logistic regression achieved a diagnostic accuracy, sensitivity, and specificity of almost 100% 78 . In a similar approach, 22 different clinical features were extracted from each of 132 subjects.…”

“…Despite these current limitations, AI/ML techniques are needed in the medical field due to the special ability to efficiently analyze and integrate large and heterogeneous data. [27, 28,52], [67,78,79], [94,102,104] Random forest Random forest is an ensemble learning method. It contains multiple decision trees and integrates these decision trees to category of data.…”

Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning

“…The distance from a sample point to the hyper plane is (19) To maximize the distance, is minimized. The training target of SVM is shown as (20) A Lagrangian is selected for optimization: (21) By setting the derivatives of L to zero with respect to ω and b, ω is obtained as follows: (22) The training target is reformulated as follows: (23) To solve the non-separable case, the regularization factors C are introduced and reformulated Eq. ( 23): (24) To reduce the operational complexity of the inner products, the kernel functions are used to replace the inner product: (25) The regular method used to obtain the coefficients is the sequential minimal optimization (SMO) algorithm [39].…”

“…Haider et al [21] use the median frequency and linear predictive coefficients combined with the spirometry parameters to classify the normal patients and COPD. The classification accuracy reaches 100%.…”

“…They found that certain individual features show good results in wheeze recognition, whereas combinations of features increase the accuracy. Haider et al [21] investigated the chronic obstructive pulmonary disease (COPD) using lung sounds and obtained excellent results. They improved the classification results to 100% by combining lung sound parameters with spirometry parameters.…”