Objective To explore the application of combined optimized machine learning algorithm for predicting the risk model of postoperative infectious complications of gastric cancer and to compare the accuracy with other algorithms, so as to find reliable biomarkers for early diagnosis of postoperative infection of gastric cancer. Methods The clinical data of 420 patients with gastric cancer at the Third Affiliated Hospital of Anhui Medical University from May 2018 to April 2023 were retrospectively analyzed and the patients were randomly divided into training set and validation set. Univariate analysis was used to determine the risk factors of postoperative infectious complications. Six conventional machine learning models are constructed using the training set: linear regression, random forest, SVM, BP, LGBM, XGBoost, and MGA-XGBoost model. The validation set was used to evaluate the seven models through evaluation indicators such as ACC, precision, ROC and AUC. Results Postoperative infectious complications were significantly correlated with age, operation time, diabetes, extent of resection, combined resection, stage, preoperative albumin, perioperative blood transfusion, preoperative PNI, LCR and LMR. Among the seven machine learning models, the MGA-XGBoost model performed best. Among the seven machine learning models, the MGA-XGBoost model performed best, with AUC of 0.936, ACC of 0.889, recall of 0.6, F1-score of 0.682, and precision of 0.79 on the validation set. Diabetes had the greatest influence on the internal structure of the model. Conclusion This study proves that the MGA-XGBoost model incorporating comprehensive inflammation indicators can predict postoperative infectious complications in patients with gastric cancer.
To address the problems of misidentification of similar gaits, excessive feature dimensionality, and computational complexity in gait recognition, this paper proposes a gait recognition method based on feature-level fusion. After validating the complementarity between motion posture signals and surface electromyography (sEMG) signals, parameters in the time, frequency, and time-frequency domains of the two types of signals were extracted. Based on the energy distribution of acceleration, angular velocity, and angle signals from motion posture signals, feature-level fusion was performed. A dual constraint strategy combining Gain-based discriminability filtering and energy-ratio stability filtering was adopted to reduce feature dimensions, yielding the most discriminative feature subset, upon which the XGBoost model was applied for gait recognition. Experimental results showed that the proposed method improved the average recognition accuracy by 8.6% over the baseline model that used only motion posture signals, reaching 95.8%. Specifically, the accuracies for forward, backward, and turning gaits reached 89.8%, 95.2%, and 97.3%, respectively, effectively reducing the misidentification rates for these three similar gaits. Furthermore, the feature-level fusion strategy effectively improved computational efficiency, and the energy distribution-based feature selection strategy reduced the impact of background noise on feature parameter perturbations, thereby enhancing model stability. This method provides strong technical support and engineering application value for gait feature parameter identification and real-time intelligent gait recognition control of exoskeletons.
ObjectiveTo construct a preoperative objective index-based model for predicting the mortality risk of aortic dissection, aiming to provide a quick risk assessment tool for primary healthcare. MethodsThe patients with aortic dissection from the Medical Information Mart for Intensive Care (MIMIC-Ⅳ) database between 2008 and 2019 were included. These patients were randomly divided into a training set, a validation set, and a test set at a ratio of 7:2:1. Based on the Akaike information criterion (AIC), forward regression was used to select the risk factors for patients with post-dissection mortality, and the XGBoost algorithm was employed to establish the prediction model. The SHAP (SHapley Additive exPlanations) theory was used for interpretive analysis. ResultsOut of the 271 patients of aortic dissection, 158 were males and 113 were females, with a median age of 70.3 (58.8, 79.5) years. The training set, validation set, and test set consisted of 189, 54, and 28 patients, respectively. During the follow-up period, 99 (36.5%) deaths occurred. Forward stepwise regression based on the AIC, clinical relevance assessment and literature review, was employed to identify 18 preoperative independent predictors. An XGBoost prediction model was constructed accordingly. After grid search optimization, the model demonstrated good discrimination in both the validation set [area under the curve (AUC)=0.681] and the test set (AUC=0.735). The SHAP analysis indicated that age (mean |SHAP|=0.081), activated partial thromboplastin time (mean |SHAP|=0.065), and red cell distribution width (mean |SHAP|=0.038) were the top three predictive contributors. ConclusionThe aortic dissection mortality risk prediction model constructed based on the XGBoost algorithm can effectively predict the incidence of mortality outcomes. Characteristic indicators such as age, activated partial thromboplastin time, and red cell distribution width can assist clinicians in identifying high-risk patients, making triage referral decisions, and optimizing preoperative interventions within the golden time window, ultimately aiming to reduce the mortality rate of patients with aortic dissection.