Ebiaredoh-Mienye Sarah A, Swart Theo G, Esenogho Ebenezer, Mienye Ibomoiye Domor
Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.
Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.
Bioengineering (Basel). 2022 Jul 28;9(8):350. doi: 10.3390/bioengineering9080350.
The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and 99.8%, respectively. Additionally, the experimental results show that the feature selection positively impacted the performance of the various classifiers. The proposed approach has produced an effective predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for effective disease detection.
慢性肾脏病（CKD）的高患病率是全球重大的公共卫生问题。该疾病死亡率高，尤其是在发展中国家。由于没有明显的早期症状，CKD常常未被发现。与此同时，早期检测和及时的临床干预对于减缓疾病进展至关重要。机器学习（ML）模型可以提供高效且具成本效益的计算机辅助诊断，以协助临床医生实现CKD的早期检测。本研究提出了一种通过结合基于信息增益的特征选择技术和成本敏感自适应提升（AdaBoost）分类器来有效检测CKD的方法。这样的方法可以节省CKD筛查时间和成本，因为诊断仅需要少数临床测试属性。所提出的方法与最近提出的CKD预测方法和知名分类器进行了基准测试。在这些分类器中，使用精简特征集训练的所提出的成本敏感AdaBoost实现了最佳分类性能，准确率、灵敏度和特异性分别为99.8%、100%和99.8%。此外，实验结果表明特征选择对各种分类器的性能产生了积极影响。所提出的方法为CKD诊断生成了有效的预测模型，并且可以应用于更多不平衡的医学数据集以进行有效的疾病检测。
