Alghamdi Maha Mesfer, Alazwary Naael H, Alsowayan Waleed A, Algamdi Mohmmed, Alohali Ahmed F, Yasawy Mustafa A, Alghamdi Abeer M, Alassaf Abdullah M, Alshehri Mohammed R, Aljaziri Hussein A, Almoqati Nujoud H, Alghamdi Shatha S, Bin Magbel Norah A, AlMazeedi Tareq A, Neyazi Nashaat K, Alghamdi Mona M, Alazwary Mohammed N
College of Applied Studies and Community Service, Department of Computer Science and Engineering, King Saud University, Riyadh, Saudi Arabia.
Department of Internal Medicine, Security Forces Hospital, Riyadh, Saudi Arabia.
IET Syst Biol. 2024 Dec;18(6):298-317. doi: 10.1049/syb2.12101. Epub 2024 Oct 29.
Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID-19 status. Four supervised machine learning algorithms-Random Forest, Bagging classifier, Decision Tree, and Logistic Regression-were employed to predict COVID-19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID-19 cases, supporting the development of computer-assisted diagnostic tools in healthcare.
人工智能在医疗保健领域发挥着关键作用，它通过加强决策制定和数据分析来实现，尤其是在新冠疫情期间。这种病毒影响所有年龄段的人，但其对老年人以及患有慢性疾病等基础健康问题的人的影响更为严重。本研究旨在开发一种机器学习模型，以改善对急性呼吸道症状患者的新冠病毒预测。使用了沙特阿拉伯两家医院915名患者的数据，根据慢性肺部疾病和新冠病毒感染状况将其分为四组。采用了四种监督式机器学习算法——随机森林、装袋分类器、决策树和逻辑回归——来预测新冠病毒感染情况。特征选择确定了12个用于预测的关键变量，包括胸部X光异常、吸烟状况和白细胞计数。随机森林模型的准确率最高，为99.07%，其次是决策树、装袋分类器和逻辑回归。该研究得出结论，机器学习算法，尤其是随机森林算法，能够有效地预测和分类新冠病毒感染病例，有助于医疗保健领域计算机辅助诊断工具的开发。
