General Surgery Department, Nhan dan Gia Dinh Hospital, 1 No Trang Long Street, Ward 7, Binh Thanh District, Ho Chi Minh City, Vietnam.
Department of Medical Statistics and Informatics, University of Medicine and Pharmacy at Ho Chi Minh City, 217 Hong Bang Street, Ward 11, District 5, Ho Chi Minh City, Vietnam.
Biomed Res Int. 2023 Apr 14;2023:5013812. doi: 10.1155/2023/5013812. eCollection 2023.
Complicated appendicitis, a potentially life-threatening condition, is common. However, the diagnosis of this condition is mainly based on physician's experiences and advanced diagnostic equipment. This study built and validated machine learning models to facilitate the detection of complicated appendicitis.
A retrospective cohort study was conducted based on medical charts of all patients undergoing a laparoscopic appendectomy at a city hospital during 2016-2020. The synthetic minority over-sampling technique (SMOTE) was used to adjust for the imbalance. Multiple classification approaches were used to train and validate models including support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), artificial neural network (ANN), and gradient boosting (GB).
Among 1,950 patients included in the data analysis, there were 483 patients identified as having complicated appendicitis (24.8%). Based on data without SMOTE adjustment for imbalance, the accuracy levels and AUCs were high in all models using different parameters, ranging from 0.687 to 0.815. After adjusting for imbalance data using SMOTE, AUC and accuracy levels in the models using imbalance adjusted data were higher. Of these, the GB had all AUC and accuracy values of approximately 0.8 or more in both adjusted and unadjusted data.
Machine learning approaches including SVM, DT, logistic, KNN, ANN, and GB have a high level of validity in classifying patients with complicated appendicitis and patients without complicated appendicitis. Among these, GB had the highest level of validity and should be used or further validated. Our study indicates the beneficial potentials of machine learning techniques in a clinical setting in general and in the diagnosis of complicated appendicitis in particular.
复杂阑尾炎是一种潜在危及生命的疾病，较为常见。然而，这种疾病的诊断主要依赖于医生的经验和先进的诊断设备。本研究构建并验证了机器学习模型，以帮助诊断复杂阑尾炎。
本研究基于一家市级医院 2016 年至 2020 年期间所有接受腹腔镜阑尾切除术患者的病历进行了回顾性队列研究。使用合成少数过采样技术（SMOTE）来调整不平衡。使用多种分类方法来训练和验证模型，包括支持向量机（SVM）、决策树（DT）、K 近邻（KNN）、逻辑回归（LR）、人工神经网络（ANN）和梯度提升（GB）。
在纳入数据分析的 1950 名患者中，有 483 名患者被诊断为患有复杂阑尾炎（24.8%）。基于未使用 SMOTE 调整不平衡的数据，使用不同参数的所有模型的准确度水平和 AUC 均较高，范围在 0.687 至 0.815 之间。使用 SMOTE 调整不平衡数据后，调整后数据模型的 AUC 和准确度水平更高。在这些模型中，GB 在调整后和未调整数据中均具有约 0.8 或更高的 AUC 和准确度值。
包括 SVM、DT、LR、KNN、ANN 和 GB 在内的机器学习方法在对患有复杂阑尾炎和不患有复杂阑尾炎的患者进行分类方面具有较高的有效性。在这些模型中，GB 的有效性最高，应予以使用或进一步验证。本研究表明，机器学习技术在一般临床环境中具有有益的潜力，特别是在复杂阑尾炎的诊断方面。
