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Abstract：
To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cross-sectional study was conducted. From January to December 2022, 158 patients underwent transnasal sellar pituitary tumor resection in three tertiary hospitals in Sichuan Province were selected as the research objects. The olfactory status was evaluated one week after surgery. They were randomly divided into a training set and a test set according to the ratio of 8:2. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. Based on different machine learning algorithms, BP neural network, logistic regression, decision tree, support vector machine, random forest, LightGBM, XGBoost, and AdaBoost were established to construct olfactory dysfunction risk prediction models. The accuracy, precision, recall, F1 score, and area under the ROC curve (AUC) were used to evaluate the model\'s prediction performance, the optimal prediction model algorithm was selected, and the model was verified in the test set of patients. Of the 158 patients, 116 (73.42%) had postoperative olfactory dysfunction. After missing value processing and feature screening, an essential order of influencing factors of olfactory dysfunction was obtained. Among them, the duration of operation, gender, type of pituitary tumor, pituitary tumor apoplexy, nasal adhesion, age, cerebrospinal fluid leakage, blood scar formation, and smoking history became the risk factors of olfactory dysfunction, which were the key indicators of the construction of the model. Among them, the random forest model had the highest AUC of 0.846, and the accuracy, precision, recall, and F1 score were 0.750, 0.870, 0.947, and 0.833, respectively. Compared with the BP neural network, logistic regression, decision tree, support vector machine, LightGBM, XGBoost, and AdaBoost, the random forest model has more advantages in predicting olfactory dysfunction in patients after transnasal sellar pituitary tumor resection, which is helpful for early identification and intervention of high-risk clinical population, and has good clinical application prospects.
摘要：
构建基于机器学习算法的经鼻鞍型垂体瘤切除术后嗅觉功能障碍预测模型。进行了横断面研究。选取2022年1-12月在四川省三家三级医院行经鼻鞍型垂体瘤切除术的158例患者作为研究对象。手术后一周评估嗅觉状态。按照8：2的比例将他们随机分为训练集和测试集。利用训练集构建预测模型,并使用测试集来评估模型的效果。基于不同的机器学习算法，BP神经网络,逻辑回归，决策树,支持向量机，随机森林,LightGBM,XGBoost,建立和AdaBoost构建嗅觉功能障碍风险预测模型。准确性,精度,召回，F1得分,和ROC曲线下面积(AUC)用于评估模型的预测性能，选择了最优的预测模型算法，并在患者测试集中对模型进行验证。158名患者中,术后嗅觉功能障碍116例(73.42%)。经过缺失值处理和特征筛选，获得了嗅觉功能障碍影响因素的基本顺序。其中,操作的持续时间，性别,垂体肿瘤的类型，垂体瘤卒中,鼻腔粘连,年龄,脑脊液漏,血疤形成,吸烟史成为嗅觉功能障碍的危险因素，是模型构建的关键指标。其中,随机森林模型的AUC最高,为0.846,精度,召回，F1评分分别为0.750、0.870、0.947和0.833。与BP神经网络相比,逻辑回归，决策树,支持向量机，LightGBM,XGBoost,和AdaBoost,随机森林模型在预测经鼻鞍区垂体瘤切除术后患者嗅觉功能障碍方面更具优势,有助于临床高危人群的早期识别和干预,具有良好的临床应用前景。
