基于迭代最近邻分析和优化学习的医疗诊断决策系统

K. Rezaee, Mohammad Hossein Khosravi, Hani Attar, Mohammed Alghanim
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引用次数: 0

摘要

隐性疾病是一种没有明确临床症状的慢性疾病,在出现不可逆损害的晚期才被诊断出来。越来越多的自动机器方法被用于早期诊断,以减少与疾病相关的并发症。然而,以前的自动化方法的性能受到不确定性、缺乏通用性和不可靠性的困扰。利用特征聚合方法和优化学习,提出了一种结合迭代邻域成分分析(iNCA)的混合模型。利用从水循环算法(WCA)进行分类方向优化的支持向量机(SVM)算法,对几种类型的疾病进行了分类,得到了最好的结果。WCA算法的一个关键特点是能够找到全局最优解。在选择特征时,该方法工作速度快,并选择具有最低错误级别的特征子集。在本研究中,将提高诊断的准确性,并减少过拟合的影响。我们从UCI数据库中获得了所需的医疗数据,该数据库包含肝炎、糖尿病、肾衰竭和乳腺癌等疾病的数据集。与过去几年发表的自动检测隐性疾病的类似方法相比,可以预见,结果将会更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A decision-making system for medical diagnosis based on iterative nearest component analysis and optimized learning
A silent disease is a chronic illness without clear clinical symptoms, and is diagnosed at an advanced stage when there is irreversible damage. Increasingly, automatic machine methods are used for early diagnosis to reduce complications associated with diseases. The performance of previous automated methods, however, was plagued by uncertainty, lack of generalizability, and unreliability. Using the feature aggregation approach and optimized learning, this paper proposes a hybrid model incorporating iterative neighborhood component analysis (iNCA). Using the support vector machine (SVM) algorithm, which has been optimized from the water cycle algorithm (WCA) in the direction of classification, the best results have been reported from the classification of several types of diseases. A key feature of the WCA algorithm is its ability to find the global optimum. When selecting features, the method works rapidly and selects the subset of features that has the lowest error level. In this research, accuracy of diagnostics will be improved and the effects of overfitting will be reduced. We obtained the desired medical data from the UCI database, which contains diseases such as hepatitis, diabetes, kidney failure, and breast cancer datasets. As compared to similar methods that have been published in the last few years for automatic detection of silent diseases, it can be predicted that the results will be better.
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