## 3 Results
For the dataset of patients and control samples with a matching ratio of 1:3, the respective probability density functions estimated by KDE are presented in Figure 3 in the format of a curve chart. Again, it can be seen that patients and control samples exhibited very different data distributions in the diagnostic records. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **OR for Accuracy** & **OR for PPV** & **OR for TPR** \\ \hline \multicolumn{3}{c}{Xpdf was generated using the Gaussian kernel.} \\ \hline SVM & 2.045 (2.390–1.750) & 1.410 (1.648–1.206) & 4.365 (5.101–3.735) \\ Decision tree & 0.996 (1.158–0.856) & 0.983 (1.143–0.845) & 1.018 (1.184–0.875) \\ NN (80, 10, 1) & 1.232 (1.443–1.053) & 1.169 (1.369–0.998) & 1.296 (1.517–1.107) \\ NN (160, 40, 1) & 0.999 (1.167–0.855) & 1.114 (1.301–0.953) & 0.839 (0.980–0.718) \\ NN (80, 20, 10, 1) & 1.380 (1.625–1.172) & 1.167 (1.375–0.991) & 1.810 (2.131–1.537) \\ \hline \multicolumn{3}{c}{Xpdf was generated using the exponential kernel.} \\ \hline SVM & 1.968 (2.298–1.686) & 1.330 (1.553–1.139) & 4.427 (5.168–3.792) \\ Decision tree & 1.024 (1.193–0.880) & 1.010 (1.176–0.868) & 1.049 (1.222–0.901) \\ NN (80, 10, 1) & 1.195 (1.399–1.020) & 1.036 (1.214–0.885) & 1.418 (1.662–1.211) \\ NN (160, 40, 1) & 1.055 (1.235–0.901) & 1.147 (1.343–0.980) & 0.897 (1.050–0.766) \\ NN (80, 20, 10, 1) & 1.266 (1.475–1.087) & 1.282 (1.493–1.100) & 1.218 (1.419–1.045) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The OR and corresponding 95% CI values between the learning model constructed using both X and Xpdf (Table 2) and the model constructed using X only (Table 3) for patients and controls with a matching ratio of 1:1. For cases and controls with a matching ratio of 1:3, the OR and corresponding 95% CI values between the learning models constructed using both X and X\({}_{\text{pdf}}\) (Table 6) and the models constructed using X only (Table 7) are presented in Table 8. Similarly, it can be observed that most of the learning models constructed using both X and X\({}_{\text{pdf}}\) performed better in terms of accuracy, PPV, and TPR than models constructed using X only. In addition, we found that when the matching ratio of cases and controls was increased to 1:3, the learning model constructed using SVM and X input attributes obviously tended to predict that the sample was negative (Table 7). Therefore, although this model achieved the best PPV of 0.719, it also had the worst TPR of 0.148. Using soft label information X\({}_{\text{pdf}}\) can significantly improve the problem caused by the unbalanced proportion of answer categories. Although the PPV values of the SVM models constructed using both X and X\({}_{\text{pdf}}\) were reduced to 0.640-0.639, their TPR was improved to 0.593-0.583 (Table 6). This phenomenon verifies that X\({}_{\text{pdf}}\) does provide information useful for identifying bipolar disorder patients. Finally, for the dataset of patients and controls with a matching ratio of 1:3, the decision tree analysis mentioned above was executed again. Similarly, we examined the decision rules accompanying the tree structures produced and counted the features in X\({}_{\text{pdf}}\) most frequently chosen as branching attributes. The disease diagnoses corresponding to these branching attributes are listed below based on the descending order of the chosen frequency. For decision rules leading to a positive label of bipolar disorder, the most frequent branching attributes included hypertension, depressive disorder, anxiety disorder, suicidal ideations, type II diabetes mellitus, esophageal reflux, hyperlipidemia, nicotine dependence, hypercholesterolemia, asthma, chest pain, hypothyroidism, and atherosclerosis/coronary heart disease.