Abstract
With the increase in the number of cardiovascular patients in China, this paper must use better detection methods to examine these patients. Blood lipid testing is a quantitative method for the determination of lipids contained in blood (plasma). Blood lipids are the general term for neutral fats (triglycerides and cholesterol) and lipids (phospholipids, glycolipids, sterols, and steroids) in plasma, which are widely present in the human body. At the same time, this paper also applies this technology to different examination procedures at various current stages. Cardiovascular disease, also known as circulatory system disease, is a series of diseases involving the circulatory system. The circulatory system refers to the organs and tissues that transport blood in the human body, mainly including the heart and blood vessels (arteries, veins, and microvessels), which can be subdivided into acute and chronic and are generally related to arteriosclerosis. In recent years, the most widely used is blood lipid measurement. Therefore, this paper proposes a study on the safety evaluation and effect analysis of blood lipid testing in the diagnosis of cardiovascular disease patients. This article focuses on the causes of cardiovascular disease and introduces the purpose, methods, results, and conclusions of blood lipid detection. Combined with the deep learning method combined with wavelet analysis and support vector machine (SVM), the in-depth analysis is carried out, and suitable patients are selected for blood lipid detection. The final experimental results showed that apolipoprotein B (APOB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and other indicators were significantly different from the control group. The values of apolipoprotein A1 (APOA1) and high-density lipoprotein cholesterol (HDL-C) were significantly different from those in the control group, P < 0.05. This shows that blood lipid examination has a good effect on the diagnosis of cardiovascular patients; it can make accurate diagnosis and prediction of the patient's condition and has good clinical application value.