Diagnosing Voice Disorder with Machine Learning

被引：0

作者：

Minh Pham ^{[1
]}

Lin, Jing ^{[2
]}

Zhang, Yanjia ^{[2
]}

机构：

[1] Univ S Florida, Ctr Urban Transportat Res, Tampa, FL 33620 USA

[2] Univ S Florida, Dept Math & Stat, Tampa, FL USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年

关键词：

Voice disorder diagnosis; Machine Learning; SVM; KNN; Gradient Boosting; Ensemble Learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The goal of this study is to build an effective model to identify the types of voice disorder, include Normal, Neoplas, Phonotrauma and Vocal palsy, from FEMH dataset. To deal with this classification problem, we use several Machine learning methods, such as Support Vector Machine, Random Forest, K-Nearest Neighbor, and Gradient Boosting. Meanwhile, we also try the Ensemble Learning algorithm, which combines these learners and selects the most popular prediction.

引用

页码：5263 / 5266

页数：4

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