Misir Rajesh, Mitra Malay, Samanta Ranjit Kumar
Department of Computer Science, Vidyasagar University, Medinipur, India.
Department of Computer Science and Application, Expert Systems Laboratory, University of North Bengal, Darjeeling, West Bengal, India.
J Pathol Inform. 2017 Jun 19;8:24. doi: 10.4103/jpi.jpi_88_16. eCollection 2017.
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs.
慢性肾脏病（CKD）是危及生命的疾病之一。为提高生存率，需要进行早期检测和妥善管理。根据UCI数据集，有24个属性用于预测CKD或非CKD。至少有16个属性需要进行病理检查，这涉及更多的资源、资金、时间和不确定性。这项工作的目的是探索能否使用较少数量的特征以合理的准确率预测CKD或非CKD。本研究采用了一种智能系统开发方法。我们尝试了一种重要的特征选择技术来发现能更好地解释数据集的简化特征。采用了两种智能二元分类技术来验证简化后的特征集。根据四个重要的分类评估参数对性能进行了评估。从我们的结果来看，我们可以更多地关注那些简化后的特征来识别CKD，从而减少不确定性、节省时间并降低成本。
