Anomalous Gait Detection using Naive Bayes Classifier

被引：0

作者：

Manap, Hany Hazfiza ^{[1
]}

Tahir, Nooritawati Md ^{[1
]}

Abdullah, R.

机构：

[1] Univ Teknol MARA UiTM, Fac Elect Engn, Shah Alam 40450, Malaysia

来源：

2012 IEEE SYMPOSIUM ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ISIEA 2012) | 2012年

关键词：

Naive Bayes; Sequential feature selection; gait analysis; Parkinson Disease;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The aim of this study is to investigate the potential of Naive Bayes classifier as abnormal gait pattern detection specifically due to Parkinson Disease since it is vital to identify the best classifier that can perform competitively prior to implementation of a gait identification system. Moreover, the significant of SFS short for 'sequential feature selection' is experimental explored along with Naive Bayes capability as classifier. Initial findings showed that classification task based on Naive Bayes is extremely competitive based on the highest accuracy rate attained specifically 93.75% through sequential feature selection and 84.38% otherwise. This finding confirmed that Naive Bayes precisely with SFS is among the most suitable classifier for detection of abnormal gait pattern in PD.

引用

页数：4

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