Application of Deep Learning Model and Machine Learning Detection Algorithm for Gastric Cancer Patients Undergoing Chemotherapy

Abstract

This work focused on exploring the application value of machine learning detection (MLD) algorithm and evidence-based nursing (EBN) in the chemotherapy (CHET) for gastric cancer (GC) patients. 100 GC patients who were treated in the Guang’an Traditional Chinese Medicine Hospital and needed postoperative CHET were recruited and randomly assigned to experimental (Exp) and control (Ctrl) groups, each including 50 patients. All participants received adjuvant CHET after gastrectomy. During CHET, participants in the Ctrl group were given routine nursing, while the experimental were given EBN in addition to routine nursing. Differences in self-rating anxiety scale (SAS), self-rating depress scale (SDS), QLQ-C30 life core questionnaire, and adverse reaction (AR) evaluation criteria were compared for participants in different groups after nursing. At the same time, all patients underwent computed tomography (CT) examination and all images were detected by MLD algorithm. After intervention, the SAS and SDS scores of patients in the Exp and Ctrl groups were 26.7±5.3 versus 33.6±6.61 and 30.07±5.58 versus 36.11±8.83, respectively. The total health status (THS) score of patients was 5.59±1.17 in Exp group and 4.53±0.96 in Ctrl group, showing P < 0.05. After intervention, great differences were observed in nausea/vomiting, decreased white blood cells (WBC), decreased haemoglobin (Hb), peripheral nerve paraesthesia (PNP), muscle and joint pain (MJP), hair loss, and other indicators between patients received EBN and routine nursing methods (P < 0.05). The MLD algorithm and EBN were of high application value in the nursing of CHET treatment for GC patients.