Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization
Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization
复制标题
用多目标元启发式优化分析帕金森病与声音变化特征的关联
DOI：
10.1016/j.mehy.2020.109722
复制
发表时间：
2020-08-01
影响因子：
4.7
通讯作者：
Alatas, Bilal
中科院分区：
文献类型：
--
作者：
Altay, Elif Varol;Alatas, Bilal
帕金森病（PD）是一种神经退行性疾病，对患者的生活质量有重要的经济和社会影响。PD的诊断是根据临床症状评估的某些标准进行的。然而，这种方法可能是不充分的，特别是在发病期间。PD的声学分析是一种经济、简单、无创的早期诊断方法。关联规则的挖掘是数据挖掘中的一个问题，其目的是在庞大的数据集中发现有价值和有趣的关联。虽然关联分析非常流行和有用，但据我们所知，还没有任何研究使用声音变化特征进行PD的关联分析。本研究旨在从包含大量数值处理语音数据的PD数据集中自动挖掘可理解、有趣和准确的关联规则。由于预处理后PD数据中声音属性的数值特征，传统的关联规则挖掘方法无法有效地应用于该问题。由于这个原因；为了在不使用任何预处理的情况下获得更好的数值关联规则挖掘性能，首次对基于人工智能的MOPNAR、NICGAR和QAR_CIP_NSGAII算法进行建模。此外，本研究将PD与声音变化特征的关联分析问题建模为考虑支持度、置信度、可理解性、兴趣度等多种互补/矛盾指标的多目标优化问题。根据得到的多目标规则集，NICGAR在平均置信度、平均CF、平均netconf、平均yulesQ和平均属性数方面表现优异。
Parkinson's disease (PD) is a neurodegenerative disorder that has important economic and social effects influencing the quality of patient life. Diagnosis of PD is performed in terms of certain criteria depending on the clinical symptom evaluation. However, this method may be inadequate, especially during the onset of the disease. Acoustic analysis of PD is a cost-effective, easy, and non-invasive method for early diagnosis. The mining of association rules is one of the problems in data mining that aims to find valuable and interesting associations in huge data sets. Although association analysis is very popular and useful, to the best of our knowledge, there is not any study on association analysis of PD using vocal change characteristics. Automatic mining of comprehensible, interesting, and accurate association rules in PD data sets containing huge numerical processed voice data is aimed in this study. Due to the numerical characteristics of the vocal attributes in pre-processed PD data, classical association rules mining methods cannot be efficiently applied to this problem. For this reason; MOPNAR, NICGAR, and QAR_CIP_NSGAII that are artificial intelligence-based algorithms were modeled for mining of numerical association rules in order to obtain better performances without using any pre-process for numerical data for the first time. Furthermore, the problem of association analysis of PD with vocal change characteristics was modeled as a multi-objective optimization problem considering many different complementary/contradictory metrics such as support, confidence, comprehensibility, interestingness, etc. in this study. According to the obtained multi-objective rule sets, the NICGAR outperformed in terms of average confidence, average CF, average netconf, average yulesQ, and average number of attributes.