Using Kernel Density Estimation in Knowledge Distillation to Construct the Prediction Model for Bipolar Disorder Patients

Abstract

Bipolar disorder is a severe mood disorder and is one of the top 20 causes of disability in the world. Although there have been numerous studies based on machine learning models for the detection of bipolar disorder patients, these works have limitations. This study used a kernel density estimation algorithm to generate distributions of the input data, which can make knowledge distillation work and can improve prediction performances of the machine learning models for bipolar disorder. To the best of our knowledge, this is the first attempt to apply kernel density estimation to knowledge distillation. Another main contribution is that we used medical history information that was readily available from the electronic health record system, trying to improve the limitation of previous studies that needed to use special instruments to collect input data. Furthermore, in view of the fact that most previous studies have sample sizes of less than 1000, we collected tens of thousands of data samples to improve the representativeness of the constructed prediction models. Finally, the generated data distributions helped the decision tree algorithm to select the appropriate branching attributes to construct the prediction models. These branching attributes can be mapped back to specific diseases that are all associated with bipolar disorder.