# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 5 Results and Discussion
### Performance Comparison with Other Works  
A generalized comparison is given in Table 7 for the reader's understanding where different techniques have been utilized with various subjects reporting a classification accuracy for various datasets utilized for audio cough classification. However, this work mainly compares its results with the Firat University Hospital dataset [51] that contains 110 individuals with acute asthma, 247 healthy individuals, 241 individuals with COVID-19 and 244 individuals with heart failure, and the results are provided.  
On examining Table 7, the proposed work gave very good results when compared to previous works, especially in reference number [51]. Though the previous work proposed a classification accuracy of 100 percent, only a single idea was concentrated on, whereas in this work, three unique ideas were implemented, and a comprehensive analysis was performed and reported. The highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with the arc-cosine ELM classifier. The second highest classification accuracy of 98.93% was obtained when DCC with EFA was implemented with the arc-cosine ELM classifier. The third highest classification accuracy of 98.86% was obtained when MIC with EFA was implemented with the arc-cosine ELM classifier. The advantage of the proposed model is that it utilizes the intrinsic properties of all the hybrid models, thereby taking advantage of all the wonderful properties of each algorithm, and a good result is produced every time. The disadvantage of the proposed model is that one must be careful in choosing the various models or algorithms, as certain combinations can produce extremely good results, and some combinations can produce  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference Number**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Subjects**} & **Accuracy (\%) or Other** \\  & & & **Performance Metric** \\  & & & **Mentioned** \\ \hline
[17] & MFCC and multi-criteria & & & \\  & decision making & 622 COVID-19 and 1610 healthy & AUC = 78.00\% \\ \hline
[21] & MFCC and DNN & 150 COVID-19, bronchitis, healthy & 90.8\% for cough data \\  & & and asthma & & \\ \hline
[22] & CNN + LSTM & 92 COVID-19 and 1079 healthy & 95.3\% \\ \hline
[23] & MFCC and DNN & 50 COVID-19 and 50 healthy & 97.5\% \\ \hline
[24] & Wavelet Transform + LDA & 26 healthy, 17 asthma and 22 chronic & 90\% \\  & & obstructive pulmonary disease & \\ \hline
[25] & MFCC and Recurrence Quantification & & & \\  & Analysis and XGBoost & 20 COVID-19 and 1468 healthy & 97\% \\ \hline
[26] & Neural networks & 9 asthma and 9 pneumonia & Kappa = 89.00\% \\ \hline
[27] & MFCC + SVM & 47 asthma and 48 healthy & 77.8\% \\ \hline
[51] & Directed Knight Pattern & 110 acute asthma, 247 healthy, & 100\% \\  & & 241 COVID-19 and 244 heart failure & 98.78\% \\ \hline Proposed work & Gabor dictionary with elastic net and & 110 acute asthma, 247 healthy, & \\  & ensemble model & 241 COVID-19 and 244 heart failure & 96.35\% \\ \hline  & SCAE & 110 acute asthma, 247 healthy, & \\  & & 241 COVID-19 and 244 heart failure & 96.35\% \\ \hline  & TQWT + AF + arc-cosine ELM & 110 acute asthma, 247 healthy, & \\  & & 241 COVID-19 and 244 heart failure & 98.34\% \\ \hline  & Sparse TQWT + AF + arc-cosine ELM & 110 acute asthma, 247 healthy, & \\  & & 241 COVID-19 and 244 heart failure & 98.99\% \\ \hline  & MIC + EFA + arc-cosine ELM & 110 acute asthma, 247 healthy, & \\  & & 241 COVID-19 and 244 heart failure & 98.86\% \\ \hline  & DCC + EFA + arc-cosine ELM & 110 acute asthma, 247 healthy, & \\  & & 241 COVID-19 and 244 heart failure & 98.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison with other works.  
very poor results. As a result, a lot of trial-and-error combination possibilities must be tried out, which might require a lot of time and energy from the researchers.