TY -的A2 -瓦伦蒂,切萨雷·f . AU - Aldhyani Theyazn H.H AU - Alshebami,穆罕默德阿里•萨利赫AU - Alzahrani y . PY - 2020 DA - 2020/03/09 TI -软聚类为加强慢性病的诊断机器学习算法SP - 4984967六世- 2020 AB -慢性疾病是严重威胁世界各地的公共卫生。据估计，它约占全世界所有死亡人数的60%，约占全球慢性病负担的43%。因此，对卫生保健数据的分析帮助卫生官员、患者和卫生保健社区对这些疾病进行早期检测。从医疗保健数据中提取模式有助于医疗保健社区获得用于诊断的完整医疗数据。本研究工作的目的是完善慢性病监测检测系统，以保障人们的生命安全。为此，本系统利用机器学习算法来增强对慢性疾病的检测。与慢性疾病相关的标准数据来自世界各地的各种资源。在医疗保健数据中，特殊慢性病包括类的模糊对象。因此，模糊对象的存在表明涉及两个或更多类的特征的可用性，这降低了机器学习算法的准确性。目前研究工作的新颖之处在于，假设采用非脆度粗糙k -均值聚类方法来解决慢性疾病数据集的模糊性，从而提高系统的性能。 The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F，用于评估拟议系统的性能。此外，本文还对提出的系统与现有机器学习算法进行了评价和比较。最后，该系统提高了机器学习算法的性能。SN - 2040-2295 UR - https://doi.org/10.1155/2020/4984967 DO - 10.1155/2020/4984967 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -