Application of data mining methods to improve screening for the risk of early gastric cancer

Abstract

BackgroundAlthough gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC.MethodsThe dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms.ResultsThe three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on.ConclusionsThe three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.

Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics

“…The recent development of machine learning offers the advantage of diagnosing gastric cancer accurately. Liu MM et al applied data mining methods to predict gastric cancer, and the accuracy was 77% [20]. Su Y et al diagnosed gastric cancer using decision tree classification of mass spectral data with an accuracy of 86.4% [21].…”

Ox-low density lipoprotein (ox-LDL) increased the susceptibility of primary nephrotic syndrome (PNS) by regulating the Treg/Th17 ratio via the MEG3 signaling

“…Furthermore, IL-10 as well as TGF-B are both related to Treg cells by playing critical and nonredundant roles in preventing the formation of atherosclerosis by means of their immunosuppressive, anti-inflammatory, as well as vasculo-protective features [18]. Naive CD4+ T cells in mice activated by TGF-β as well as IL-6 result in the activation of retinoic acid-related orphan nuclear receptor γt (RORγt), which controls the differentiation of Th17 cells [19].…”

Artificial intelligence in gastric cancer: a translational narrative review

“…This varies from simple ideas like age, gender, diet to more complex like symptoms, history, or genetics. Incorporating datamining techniques, Liu et al recruited 618 patients and devised a questionnaire that included demographic characteristics, eating habits, symptoms and family history and additionally serological examination and gastroscopy (38). Keywords and topics of interest were eventually whittled down to make a list of 34 standout factors that could be used to make predictive models.…”