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

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

The advancement of medical diagnostic techniques should not rely solely on the improvement of diagnostic accuracy, but should also rely on the study of diagnostic markers or diagnostic basis that are more effective. Therefore, research on the prediction of diagnostic markers is of great significance. The traditional method of studying diagnostic markers is often to collect dozens of sample data, and to predict the diagnostic markers according to the regression model, it is difficult to synthesize large quantities of samples. The above situation has caused the traditional research methods to have certain blindness and subjectivity. In order to solve the above series of problems, an auxiliary diagnostic system that can automatically provide disease markers while automatically diagnosing diseases is of positive significance for the development of medical level. Hyperlipidemia refers to the excessive level of blood lipids, which can directly cause some diseases that seriously endanger human health, such as coronary heart disease, atherosclerosis and so on. However, due to the absence of obvious symptoms and abnormal signs, the diseases mentioned above have strong concealment and are difficult to be detected purposefully. At the same time, with the continuous development of medical level, researchers have found that more and more diseases are highly related to hyperlipidemia, such as AIDS, depression and so on. 26, 27 Therefore, in the world, hyperlipidemia has become one of the most important diseases threatening human life and health to a large extent. Although there is no uniform international standard for the diagnosis of hyperlipidemia, hematological parameters are widely used in the diagnosis of hyperlipidemia and the evaluation of treatment methods, which is capable of use hematological parameters to automatically diagnose hyperlipidemia. [28] [29] [30] [31] In this paper, we sought to propose an auxiliary diagnosis algorithm that can not only diagnose hyperlipidemia rapidly and accurately according to human hematological parameters but also provide diagnostic markers automatically, which improves the objectivity of traditional methods and the interpretability of deep learning model algorithm. Compared with previous work, our proposed new model not only automatically determines the patient's health but also automatically provides diagnostic markers. Compared with the auxiliary diagnostic system that only provides the diagnosis result, the new model proposed in this paper has higher interpretability and credibility. Therefore, the above model can not only speed up the patient's medical treatment process but also further improve research efficiency of diagnostic markers, and have great potential for discovering new diagnostic markers. Artificial intelligence aided diagnosis system can effectively simplify the process of patients seeking medical treatment, alleviate the contradiction of lack of medical resources, and improve the survival rate of emergency patients, as shown in Figure 1 .