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Boosted classifier and features selection for enhancing chronic kidney disease diagnose

机译：增强分类器和功能选择以增强慢性肾脏疾病的诊断

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Chronic kidney disease (CKD) is a disease caused by degeneration function of the kidneys. CKD is top ten leading causes of death in the world. There are two leading causes of CKD, i.e. diabetes and hypertension. When the symptoms become more severe, the disease can only be treated with dialysis and kidney transplantation. This disease can be treated if the diagnose is conducted appropriately and quickly. However, the signs and symptoms are often not specific. Because of that, diagnosis from medical personnel is may subjective and vary. This study developed machine learning method using ensemble learning and feature selection to improve the quality of CKD diagnosis. The CKD dataset was taken from UCI machine learning repository, it contain 400 instances. CKD dataset have 24 attributes including signs, symptoms and risk factors that might appear due to CKD. In this study, features were selected using a Correlation-based Feature Selection (CFS) and AdaBoost was used for ensemble learning to improve the detection of CKD. K-Nearest Neighbour algorithm (kNN), Naive Bayes and Support Vector Machine (SVM) was used as base classifier. Overall, the best result was achieved by combination of kNN classifier with CFS and AdaBoost, with 0.981 accuracy rate, 0.980 recall rate and 0.980 f-measure rate. Highest precision rate was achieved by the combination of Naive Bayes classifier with CFS and AdaBoost, with 0.981 precision rate.
机译：慢性肾脏疾病（CKD）是由肾脏的变性功能引起的疾病。 CKD是世界上十大主要死因。导致CKD的主要原因有两个，即糖尿病和高血压。当症状变得更严重时，只能通过透析和肾脏移植来治疗该疾病。如果正确，迅速地进行诊断，则可以治疗该疾病。但是，体征和症状通常不明确。因此，医务人员的诊断可能是主观的并且会有所不同。这项研究开发了使用集成学习和特征选择的机器学习方法，以提高CKD诊断的质量。 CKD数据集取自UCI机器学习存储库，其中包含400个实例。 CKD数据集具有24个属性，包括可能因CKD出现的体征，症状和危险因素。在这项研究中，使用基于关联的特征选择（CFS）选择特征，并将AdaBoost用于集成学习以改善CKD的检测。 K最近邻算法（kNN），朴素贝叶斯和支持向量机（SVM）被用作基础分类器。总体而言，将kNN分类器与CFS和AdaBoost结合使用可获得最佳结果，准确率为0.981，召回率为0.980，f测量率为0.980。通过将朴素贝叶斯分类器与CFS和AdaBoost结合使用，可以达到0.981的最高准确率。

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