Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization

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.