Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation.
Electrical Energy Department, Balqa Applied University, Amman 11937, Jordan.
J Integr Med. 2022 May;20(3):252-264. doi: 10.1016/j.joim.2022.02.007. Epub 2022 Feb 26.
This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery.
A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia.
Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%.
The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.
本研究旨在开发专家模糊逻辑模型，以协助医生在手术前预测前列腺增生的术后并发症。
开发了一种分类手术风险的方法。利用生物活性点（穴位）电流-电压特性的旋转来形成分类器描述符。该方法用于确定在周期性暴露于锯齿形探针电流时，穴位的电阻可逆和不可逆变化。然后，在预测良性前列腺增生的手术治疗效果方面对所开发的方法进行了测试。
输入描述符是从包括 5 个穴位的电流-电压特性在内的收集数据中获得的，由 27 个输入模型的数组组成。在对照样本中，分类器对手术成功的最大诊断灵敏度为 88%，对测试数据集的预测准确性为 97%。
在分类器中使用穴位电流-电压特性描述符的元组可以用于以令人满意的准确度预测手术治疗的效果。该模型可以成为支持医生诊断的有价值工具。
