지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2018 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.10 No.1
- 발행연도
- 2018.6
- 수록면
- 355 - 358 (4page)
이용수
초록· 키워드
오류제보하기
The aim of the paper is to discriminate the Parkinson’s disease(PD) from other nerological diseases such as Amyotrophic Lateral Sclerosis (ALS), Huntington’s disease (HD),using different feature selction technique such as Prinicipal Componet Analysis and Recursive Feature Elimination, and also different classification approaches that includes linear classifiers,nonlinear classifiers,and Probabilistic classifier. Mostly the neurological disease affect the brain cells and spinal cords and that lead to the abnormality in the gait behaviour.Recetly the gait analysis caught much attention for predciting the neurological diseases such as PD, ALS and HD.With the advent of intelligent machine learning technique an attempt has been made in this paper to discriminate the PD from other neurological patients. In this paper the gait dynamics data of pateints suffered with PD,ALS and HD has been collected from the public database and a binary classification approach has been used by taking PD as one group and ALS+HD as the other group.Our results founds highest accuracy of 96.78% using SVM with Radial Basis Function(RBF) combined with reduced feature sets obtained from RFE compared to the other classifiers with feature sets from PCA.The result provided enough evidence that can asssit medical practitioners to distinguish PD from other neurological diseases by using the gait dynamics data.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. METHODOLOGY
Ⅲ. RESULTS AND DISCUSSION
Ⅳ. OUTCOME AND CONCLUSION
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2018-004-003100793