Abstract
Early diagnosis of cancer is beneficial in the formulation of the best treatment plan; it can improve the survival rate and the quality of patient life. However, imaging detection and needle biopsy usually used not only find it difficult to effectively diagnose tumors at early stage, but also do great harm to the human body. Since the changes in a patient’s health status will cause changes in blood protein indexes, if cancer can be diagnosed by the changes in blood indexes in the early stage of cancer, it can not only conveniently track and detect the treatment process of cancer, but can also reduce the pain of patients and reduce the costs. In this paper, 39 serum protein markers were taken as research objects. The difference of the entropies of serum protein marker sequences in different types of patients was analyzed, and based on this, a cost-sensitive analysis model was established for the purpose of improving the accuracy of cancer recognition. The results showed that there were significant differences in entropy of different cancer patients, and the complexity of serum protein markers in normal people was higher than that in cancer patients. Although the dataset was rather imbalanced, containing 897 instances, including 799 normal instances, 44 liver cancer instances, and 54 ovarian cancer instances, the accuracy of our model still reached 95.21%. Other evaluation indicators were also stable and satisfactory; precision, recall, F1 and AUC reach 0.807, 0.833, 0.819 and 0.92, respectively. This study has certain theoretical and practical significance for cancer prediction and clinical application and can also provide a research basis for the intelligent medical treatment.
Highlights
As one of the most threatening diseases to human health, cancer can bring great pain and psychological pressure to patients but can bring heavy economic burden to countless families and even the whole of society
Among the average entropies of three types of samples, those of a normal person are the biggest, and those of liver cancer patients are the smallest, which indicates that different cancer patients have different levels of serum protein complexity, approximate entropy and sample entropy can reflect the complexity of different serum protein sequence, and the complexity of serum protein in normal people is obviously higher than that in cancer patients, which means that cancer cells may reduce the complexity of serum proteins
The results of this study show that there are significant differences in approximate entropy and sample entropy of serum protein indexes among normal people, patients with liver cancer and patients with ovarian cancer
Summary
As one of the most threatening diseases to human health, cancer can bring great pain and psychological pressure to patients but can bring heavy economic burden to countless families and even the whole of society. Cancer is an immune disease caused by the uncontrolled growth and division of abnormal cells in the body and the spread to the whole body [1]. Diagnosis (prediction) of cancer can help physicians decide a treatment plan, which has important and positive significance for the adequate and effective treatment of cancer. Accurate prediction of cancer is very critical in the treatment of cancer. The early diagnosis of cancer is a very difficult task; once the symptoms of cancer appear, it is usually in advanced stages and is difficult to treat. Cancer recognition mainly depends on gene test or protein test, among which gene tests are inherited and static, which are mostly used in the detection of congenital genetic diseases, and cannot reflect the occurrence of diseases in the body in terms of autoimmunity and metabolism; genetic tests are difficult to interpret and are expensive
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