4.7 Article

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson's Disease Detection

期刊

JOURNAL OF PERSONALIZED MEDICINE
卷 12, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/jpm12010055

关键词

Parkinson's disease; multi-level feature selection; optimized KNN

资金

  1. Xiamen University Malaysia Research Fund [XMUMRF/2019-C4/IECE/0012]

向作者/读者索取更多资源

This study proposes a new approach based on multi-level feature selection to detect Parkinson's disease by analyzing voice recordings. Feature selection was performed using the Chi-square, L1-Norm SVM, and ReliefF algorithms, and machine learning was done using the KNN classifier. The proposed approach achieved a classification accuracy of 95.4%.
Parkinson's disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people's daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据