## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Algorithm** & **TP** & **FP** & **TN** & **FN** & **Accuracy** & **PPV** & **NPV** & **TPR** & **TNR** \\ \hline \multicolumn{10}{c}{\(\mathrm{X_{pdf}}\) was generated using the Gaussian kernel.} \\ SVM & 1650 & 463 & 1543 & 344 & 0.798 & 0.781 & 0.818 & 0.827 & 0.769 \\ Decision tree & 1569 & 434 & 1572 & 425 & 0.785 & 0.783 & 0.787 & 0.787 & 0.784 \\ NN (80, 10, 1) & 1612 & 430 & 1562 & 347 & 0.803 & 0.789 & 0.818 & 0.823 & 0.784 \\ NN (160, 40, 1) & 1605 & 397 & 1597 & 394 & 0.802 & 0.802 & 0.802 & 0.803 & 0.801 \\ NN (80, 20, 10, 1) & 1620 & 485 & 1514 & 276 & 0.805 & 0.770 & 0.846 & 0.854 & 0.757 \\ \hline \multicolumn{10}{c}{\(\mathrm{X_{pdf}}\) was generated using the exponential kernel.} \\ SVM & 1654 & 492 & 1514 & 340 & 0.792 & 0.771 & 0.817 & 0.829 & 0.755 \\ Decision tree & 1579 & 425 & 1581 & 415 & 0.790 & 0.788 & 0.792 & 0.792 & 0.788 \\ NN (80, 10, 1) & 1602 & 482 & 1554 & 315 & 0.798 & 0.769 & 0.831 & 0.836 & 0.763 \\ NN (160, 40, 1) & 1620 & 389 & 1633 & 372 & 0.810 & 0.806 & 0.814 & 0.813 & 0.808 \\ NN (80, 20, 10, 1) & 1584 & 432 & 1563 & 401 & 0.791 & 0.786 & 0.796 & 0.798 & 0.783 \\ \hline \end{tabular}
\end{table} 
Table 2: The evaluation results of the test set for patients and controls with a matching ratio of 1:1; both X and \(\mathrm{X_{pdf}}\) were used as the input attributes. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Algorithm** & **TP** & **FP** & **TN** & **FN** & **Accuracy** & **PPV** & **NPV** & **TPR** & **TNR** \\ \hline SVM & 1044 & 413 & 1593 & 950 & 0.659 & 0.717 & 0.626 & 0.524 & 0.794 \\ Decision tree & 1563 & 425 & 1581 & 431 & 0.786 & 0.786 & 0.786 & 0.784 & 0.788 \\ NN (80, 10, 1) & 1549 & 483 & 1484 & 432 & 0.768 & 0.762 & 0.775 & 0.782 & 0.754 \\ NN (160, 40, 1) & 1612 & 444 & 1533 & 332 & 0.802 & 0.784 & 0.822 & 0.829 & 0.775 \\ NN (80, 20, 10, 1) & 1505 & 526 & 1449 & 464 & 0.749 & 0.741 & 0.757 & 0.764 & 0.734 \\ \hline \end{tabular}
\end{table} 
Table 3: The evaluation results of the test set for patients and controls with a matching ratio of 1:1; only X was used as the input attributes. results for when the learning model constructed using both X and Xpdf was regarded as model 1 and the model constructed using X only was regarded as model 2 are presented in Table 4. It can be observed that most of the learning models constructed using both X and Xpdf performed better in terms of accuracy, PPV, and TPR than models constructed using X only. In order to confirm that the data distributions generated by KDE played a role in knowledge distillation, we randomly selected patients and matched control samples to form the dataset and repeated this 10 times. Each time we used KDE to generate the soft label data Xpdf, and then the Xpdf data were utilized to train a decision tree. Finally, we examined the decision rules accompanying the tree structure and counted the features in Xpdf most frequently chosen as branching attributes. The disease diagnoses corresponding to these branching attributes based on the descending order of the chosen frequency are listed below. 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, hyperlipidemia, esophageal reflux, chest pain, nicotine dependence, asthma, hypercholesterolemia, hypothyroidism, and alcohol abuse. For decision rules leading to a negative label of bipolar disorder, the most frequent branching attributes included hypertension, hyperlipidemia, type II diabetes mellitus, chest pain, alcohol abuse, esophageal reflux, atrial fibrillation, hypercholesterolemia, depressive disorder, atherosclerosis/coronary heart disease, abdominal pain, urinary tract infection, hypothyroidism, nicotine dependence, headache, and syncope and collapse.