Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically

Liu, Yuliang; Zhang, Quan; Zhao, Geng; Liu, Guohua; Liu, Zhiang

Despite it is potential, it still has limitations. One limitation of our study is that the data we used included only a few human hematological parameters. Some diseases can not only be determined by these parameters but also need other information, such as biochemical testing and so on. Diseases may also be associated with other physiological parameters that are not part of the training set. Another limitation is that the diagnosis of many chronic diseases is also related to many other types of information, such as sex, age, disease history, family history and so on. Finally, because the experimental data were collected in the metabolic disease hospital, there were many samples with metabolic diseases in the training data, which was also a factor limiting the further improvement of the performance of the model. Therefore, in the future work, we will study how to add more types of parameters to the auxiliary diagnostic system and collect more samples of different health status, so as to further improve the performance of the model. In the future work, we will also research more types of model in order to find more effective model can process human physiological parameters.