Abstract
In this paper, through a series of analysis and testing of breast cancer detection data, the statistical rules of multiple objects and multiple indicators are analyzed in the case of their correlation. First of all, univariate diagnosis and multivariate diagnosis were performed on the data. Among them, when studying the correlation between variables, it was found that HOMA had a clear linear positive correlation with insulin content in blood. It is worth noting that some patients with breast cancer show a high degree of insulin resistance and blood insulin content, which is a feature not found in samples without breast cancer. Then, through single factor analysis of variance, we believe that there were significant differences in blood test conditions, ages, and BMI indicators of samples of different health conditions. Next, the principal component analysis was used to reduce the dimension of the data. In this study, the differences in age, BMI, and blood component content between the two groups with different health conditions can be summarized by these two independent factors. Among them, the absolute value of the MCP-1 (monocyte chemoattractant protein 1) coefficient in the main component 1 is large, reflecting the characteristics of the blood component of the sample; the load values of glucose and leptin in the main component 2 are large, reflecting similar results. Then, assuming the use of m = 3 factor model and the use of maximum likelihood method and principal component method, the original data and factor rotation data are re-analyzed, so that the variables are reduced to 3 factors for analysis. Among them, the maximum likelihood method is used to estimate the factor rotation data. The first factor reflects the insulin resistance factor attributed to insulin and HOMA indicators, and the second factor reflects the body fat and thin factor attributed to BMI and leptin. The third factor reflects the glucose content in the blood. Finally, by setting different misjudgment costs for discriminant analysis, the obtained APER is 0.1638 and EAER is 0.1872. Among them, the probability of discriminating patients with breast cancer from not having breast cancer is 0.09375, which is a low rate of misjudgment and also means the model established in this paper is efficient.
Highlights
Breast cancer is becoming a leading cause of death among women in the whole world, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients [1]
The results show that the blood glucose content is concentrated at 75-125 mg/dL, and there are large values, such as 201 mg/dL, but the number is very small; the blood insulin content, adiponectin content, resistin content and MCP1 The content and Homeostatic model assessment (HOMA) indicators are similar
Through a series of analysis and testing of breast cancer detection data, the main conclusions are: 1) HOMA is one of the evaluation indicators for testing insulin resistance in the blood, and it has a clear linear correlation with the insulin content in the blood, as shown in Figure 2, which shows that the stronger the insulin resistance, the less likely the insulin in the blood is Use, so its content in the blood will be correspondingly higher
Summary
Breast cancer is becoming a leading cause of death among women in the whole world, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients [1]. The breast cancer incidence and mortality rates among Chinese women were increasing rapidly, especially in rural area during the recent 10 years, though they were still in low level worldwidely. Yang Ling et al estimated and predicted the incidence and mortality of breast cancer in China in recent years using a log-linear model, and concluded that due to the multiple effects of risk factors, population growth and aging, breast cancer will be one of the most growing malignant tumors in China [3].
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