Chou Wen-Yi, Cheng Jai-Hong, Lien Yu-Jui, Huang Tian-Hsiang, Ho Wen-Hsien, Chou Paul Pei-Hsi
Doctoral Degree Program in Biomedical Engineering, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Department of Orthopedic Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Orthop J Sports Med. 2024 Mar 5;12(3):23259671241231609. doi: 10.1177/23259671241231609. eCollection 2024 Mar.
Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment.
To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting.
Case-control study.
Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier.
A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm.
The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT.
尽管有证据表明体外冲击波疗法（ESWT）在治疗钙化性肩袖肌腱炎方面有效，但许多病例仍报告有不完全吸收和效果不理想的情况。在过去十年中，数据挖掘技术已应用于医疗保健领域，以预测疾病和治疗的结果。
确定用于预测ESWT诱导的肩部钙化吸收的理想数据挖掘技术以及用于临床环境的最准确算法。
病例对照研究。
纳入接受ESWT治疗的疼痛性钙化性肩袖肌腱炎患者。采用七个与肩部钙化相关的临床因素作为输入属性：性别、年龄、患侧、症状持续时间、治疗前Constant-Murley评分以及钙化大小和类型。评估的5种数据挖掘技术为多层感知器（神经网络）、朴素贝叶斯、序列最小优化、逻辑回归和J48决策树分类器。
本研究共纳入248例钙化性肩袖肌腱炎患者。症状持续时间较短的增益率最高（0.374），其次是钙化大小较小（0.336）和钙化类型（0.253）。采用J48决策树方法，通过10倍交叉验证，3个输入属性的准确率为89.5%，表明准确率令人满意。使用J48决策树的治疗算法表明，症状持续时间≤10个月是钙化吸收的最积极指标，其次是钙化大小≤10.82 mm。
J48决策树方法在预测ESWT治疗肩部钙化吸收方面表现出最高的精度和准确性。症状持续时间≤10个月或钙化大小≤10.82 mm代表ESWT后最可能出现吸收的临床情况。
