PDF(Pubmed)
Abstract：
Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.
摘要：
数据分类是医疗数据中预测和检测疾病的首要问题；因此，它被应用于现代医疗保健信息学。在现代信息学中，机器学习和深度学习模型在对医疗数据进行分类和改善疾病检测方面受到了极大的关注。然而,现有的技术，例如具有高维度的特征，计算复杂性，和长期执行持续时间，提出根本问题。这项研究提出了一种新颖的分类模型，采用元启发式方法来最大程度地提高对慢性肾脏疾病诊断的有效阳性。医疗数据最初被大规模预处理，用各种机制净化数据，包括缺失值解析，数据转换,并采用规范化程序。这些过程的重点是利用对缺失值的处理并准备用于深入分析的数据。我们采用二进制灰狼优化方法，使用元启发式的可靠子集选择功能。该手术旨在提高疾病预测的准确性。在分类步骤中，该模型采用具有隐藏节点的极限学习机，通过数据优化来预测CKD的存在。完整的分类器评估采用既定的措施，包括召回,特异性，kappa,F分数,和准确性，除了功能的选择。与研究相关的数据表明，所提出的方法记录了高水平的准确性，比现有的模型更好。
