Prediction of health screening data with personal uncertainty using bayesian neural networks
Authors: Yusuke Osawa, Shohei Tawata, Keiichi Watanuki, Kazunori Kaede
Abstract: In Japan, a specific health checkup has been conducted for people aged 40 and over since 2008. In the specific health checkup, public health nurses and dietitians provide health guidance according to the level of health based on the results of test values and questionnaire items. However, the content of the guidance for lifestyle improvement varies depending on the experience and judgment of the instructor. And the guidance is based only on the health checkup result report. Therefore, the evidence for lifestyle improvement is weak and does not lead to actual lifestyle improvement.In order to solve this problem, we have developed a system that predicts future test values based on past test values. In our previous research, we constructed a highly accurate model for predicting the next year's test values based on the past's health checkup test using a multichannel deep convolutional neural network, which is a type of machine learning. In addition, the features as the basis for the model's predictions were analyzed by sensitivity analysis in order to evaluate the validity of the predictions. However, there are the cases that some examinees may suddenly change their lifestyles, even if similar trends were observed in the previous year. In addition, even if the same health guidance was given in the previous year, the awareness of lifestyle improvement differ among individuals. In addition, even if the same health guidance was given in the previous year, the awareness of lifestyle improvement differ among individuals. The questionnaire includes items that ask about the awareness of lifestyle improvement, and these items have variations in the actual degree of lifestyle improvement among individuals. Thus, there is uncertainty in the prediction of test values for each examinee. In order to be convincing and safe, it is necessary considering the uncertainty of the previous year's data in predicting test values. The purpose of this study is to predict the test value considering the uncertainty of the test value due to the awareness of lifestyle improvement among examinees. A bayesian neural network (BNN) is a network that considers the weights in a neural network as random variables generated based on a certain probability distribution. We constructed a highly accurate BNN prediction model using the health checkup results concluding the basic data (age and sex), the test values of the past two years, and the results of questionnaire items as inputs, and the test values of the next year as outputs. The incidence of lifestyle diseases are predicted based on these diagnostic criteria using the BNN model outputs. In the future, we will compare the prediction of disease onset by the BNN model with that by the conventional neural network model using the actual diagnosis results of lifestyle diseases, and examine the explanatory of the two models.
Keywords: Healthcare, special health check-up, lifestyle disease, ultra-high age society, regional topics, mechanical learning, bayesian neural networks
Cite this paper: