## 3 Results
### Discrimination between Older NSR and CHF Participants
Note: SD, standard deviation; 2D, two optimal MSE scales; 3D, three optimal MSE scales; 4D, four optimal MSE scales; 5D, five optimal MSE scales. using LDA in 2D, SVM in 3D, and KNN in 5D. Overtraining occurred for eight conditions, labeled NA in Table 3, and the results showed marked overtraining for KNN. This finding suggests that the KNN classifier combined with MSE analysis is not appropriate for differentiating between older CHF and NSR participants with fewer dimensions. On average, LDA showed better accuracy than SVM or KNN. LDA accuracies were 94.4, 90.1, and 93.0% for data sample sizes of 40,000, 20,000, and 10,000, respectively. The utilization of an \(r\) of 0.15 and a 3D exhaustive search also yielded the highest accuracies. Combinations of the three MSE scales (3D) and their performances are listed in Table 4. Moreover, the combinations of [3, 6, 12] for 40,000 points, [2, 6, 9] for 20,000 points, and [1, 9, 18] for 10,000 points were associated with 100% Spe and PPV. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Data Sample Sizes** & **Scales** & **Acc** & **Sen** & **Spe** & **PPV** & **NPV** \\ \hline
40,000 & [3, 6, 12] & 94.4 & 84.0 & 100.0 & 100.0 & 92.0 \\ \hline
20,000 & [1, 6, 12] & 90.1 & 76.0 & 97.8 & 95.0 & 88.2 \\  & [1, 6, 13] & 90.1 & 76.0 & 97.8 & 95.0 & 88.2 \\  & [1, 6, 18] & 90.1 & 76.0 & 97.8 & 95.0 & 88.2 \\  & [2, 6, 9] & 90.1 & 72.0 & 100.0 & 100.0 & 86.8 \\ \hline
10,000 & [1, 9, 18] & 93.0 & 80.0 & 100.0 & 100.0 & 90.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of combinations of three optimal MSE (\(r=0.15\)) scales for the discrimination between older NSR and CHF participants using LDA, as well as the corresponding accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**LDA**} \\ \hline  & & **2D** & & **3D** & & & **4D** & & & **5D** & \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & 88.7 & 88.7 & 90.1 & 90.1 & 94.42 & 91.5 & 91.5 & 93.0 & 93.0 & 91.5 & 93.0 & 93.0 \\
20,000 & 83.1 & 84.5 & 84.5 & 87.3 & 90.1 2 & 88.7 & 88.7 & 90.1 & 87.3 & 88.7 & 90.1 & 88.7 \\
10,000 & 83.1 & 84.5 & 84.5 & 90.1 & 93.0 2 & 90.1 & 88.7 & 93.0 & 93.0 & 90.1 & 93.0 & 93.0 \\ Mean \(\pm\) SD & \multicolumn{6}{c}{85.8 \(\pm\) 2.5} & \multicolumn{6}{c}{90.6 \(\pm\) 2.0} & \multicolumn{6}{c}{90.9 \(\pm\) 2.1} & \multicolumn{6}{c}{91.2 \(\pm\) 1.7} \\ \hline  & & \multicolumn{6}{c}{**SVM**} \\ \hline  & & **2D** & & **3D** & & & **4D** & & & **5D** & \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & 85.9 & 87.3 & 85.9 & 90.1 & 88.7 & 87.3 & 90.1 & 90.1 & 87.3 & 91.5 & 90.1 & 87.3 \\
20,000 & 80.3 & 81.7 & 81.7 & 83.1 & 88.7 & 83.1 & 85.9 & 88.7 & 83.1 & 87.3 & 88.7 & 85.9 \\
10,000 & 80.3 & 81.7 & 78.9 & 87.3 & 90.1 & 88.7 & 87.3 & 91.5 & 91.5 & 88.7 & 91.5 & 90.1 \\ Mean \(\pm\) SD & \multicolumn{6}{c}{82.6 \(\pm\) 2.8} & \multicolumn{6}{c}{87.5 \(\pm\) 2.5} & \multicolumn{6}{c}{88.4 \(\pm\) 2.6} & \multicolumn{6}{c}{89.0 \(\pm\) 1.8} \\ \hline  & & \multicolumn{6}{c}{**KNN**} \\ \hline  & & **2D** & & **3D** & & & **4D** & & & **5D** & \\ \(r\) & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** & **0.10** & **0.15** & **0.20** \\ \hline
40,000 & NA [3] & NA & NA & 84.5 & 84.5 & 85.9 & 85.9 & 88.7 & 85.9 & 85.9 & 85.9 \\
20,000 & 77.5 & NA & NA & 81.7 & NA & NA & 83.1 & 76.1 & 81.7 & 84.5 & NA & 83.1 \\
10,000 & 77.5 & 78.9 & 85.9 & 84.5 & 84.5 & 84.5 & 83.1 & 87.3 & 88.7 & 84.5 & 88.7 & 90.1 \\ Mean \(\pm\) SD & \multicolumn{6}{c}{79.9 \(\pm\) 3.5} & \multicolumn{6}{c}{84.3 \(\pm\) 1.2} & \multicolumn{6}{c}{84.5 \(\pm\) 3.8} & \multicolumn{6}{c}{86.1 \(\pm\) 2.1} \\ \hline \hline \end{tabular}
\end{table}