Elashmawi Walaa H, Djellal Adel, Sheta Alaa, Surani Salim, Aljahdali Sultan
Department of Computer Science, Suez Canal University, Ismailia 41522, Egypt.
Department of Computer Science, Misr International University, Cairo 11828, Egypt.
Diagnostics (Basel). 2024 Dec 14;14(24):2822. doi: 10.3390/diagnostics14242822.
: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. : This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. : The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. : The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.
在美国，慢性阻塞性肺疾病（COPD）是一个重要的死亡原因。据我们所知，它是一种慢性炎症性肺部疾病，会切断肺部的气流。关于这种疾病，已经报告了许多症状：呼吸问题、咳嗽、喘息和黏液分泌。COPD患者可能处于危险之中，因为他们更容易患心脏病和肺癌。
本研究回顾了利用各种机器学习（ML）分类器诊断COPD的情况，这些分类器包括逻辑回归（LR）、梯度提升分类器（GBC）、支持向量机（SVM）、高斯朴素贝叶斯（GNB）、随机森林分类器（RFC）、K近邻分类器（KNC）、决策树（DT）和人工神经网络（ANN）。这些模型被应用于一个包含1603名患者的数据集，这些患者在接受肺功能测试后被转诊。
随机森林分类器（RFC）取得了卓越的准确率，在训练中达到了82.06%，在测试中达到了70.47%。此外，它在训练和测试中都取得了最高的F分数，受试者工作特征曲线（ROC）值为0.82。
利用这些机器学习模型获得的结果与该领域之前的工作一致，训练准确率在67.81%至82.06%之间，测试准确率在66.73%至71.46%之间。
