###### Abstract
In summary, since these previous studies used different datasets to construct models for identifying bipolar disorder patients, we cannot directly compare their evaluation results. However, almost all of their feature data need to be obtained through special medical instruments or biochemical tests, such as immune-inflammatory signature, blood samples, magnetic resonance imaging, electroencephalography, genomic data, etc. [6]. Therefore, the lack of easy access to analytical data presents a barrier to the development of machine learning models. On the other hand, among these 33 studies, only 2 of them (6%) had a sample size greater than 2000. The analysis data of all remaining studies contained less than 1000 samples [6]. Theoretically, if the analysis sample size is smaller, the representativeness of the contained data will be limited. This may be why the accuracy ratios of these studies are very inconsistent. There is an intuitive way to improve the performance of machine learning. Different models can be trained using the same dataset, and their prediction outcomes will then be integrated. Therefore, the idea of \"knowledge distillation\" has been proposed and has been verified in various studies. For the practice of knowledge distillation, firstly a sophisticated model or multiple models will be trained using any learning algorithm. Outcomes produced by this group of \"teacher models\" can be thought of as conditional distributions for the input data and may be referred to as \"soft labels.\" These data distributions can be used as the learning targets for the \"student model,\" which will be trained using simpler learning architectures [11; 12]. On the other hand, the soft labels can be used as the reference information and to train the student model together with the original input data. This process may also be seen as the student model \"distilling\" the \"knowledge\" provided by the group of teacher models [13]. Kernel density estimation (KDE), which is a nonparametric estimation approach in statistics, has been widely exploited to identify distributions in various types of datasets. A kernel density estimator generates an approximate probability density function (PDF) by computing the linear combination of the weighted kernel functions placed at the locationsof all data instances in the vector space [14; 15; 16]. Inspired by the aforementioned previous research, we attempted to improve their limitations while developing machine learning models for the diagnosis of bipolar disorder. Firstly, the type of data analyzed in this study is medical history information, which is readily available from the electronic health record system. Secondly, the dataset of this study contain tens of thousands of samples, which is much larger than the sample size of existing studies, thus improving the representativeness of the constructed models. Moreover, based on the concept of knowledge distillation, the PDF values produced by the KDE method were transferred as the soft labels to construct prediction models of bipolar disorder using various machine learning methods. According to the evaluation results, using the data distribution information generated by KDE did indeed improve the predictive performance of the diagnostic model for bipolar disorder. In addition, the branching attributes selected by the decision trees were mapped back to specific disease diagnoses that are all associated with bipolar disorder. To the best of our knowledge, this study is the first attempt to apply KDE to knowledge distillation for supervised machine learning.