Automated gender‐Parkinson&apos;s disease detection at the same time via a hybrid deep model using human voice

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Summary

Gender and Parkinson disease (PD) identifications are critical parts to be noted from a given in human voice. Numerous artificial intelligence based methods have been proposed to detect gender and PD easily in literature. It is purposed to build an effective and a dependable simultaneously gender and PD recognition system based on feature extraction and feature selection methods in this study. First, CNN structure is used for obtaining deeper features from TQWT applied data and acoustic deep parameters are obtained by it. Later, these deep features are subjected to mRMR feature selection algorithm that increase the performance efficiency of the classifiers. As a result, the crucial features obtained by this hybrid structure and significant success rate 98.9% is obtained with the k‐NN classifier. Thus, gender and PD are detected at the same time. Also, this work is multiclass problem so, the other success parameters are calculated separately.

Related collections

Most cited references 44

Record: found
Abstract: found
Article: found

Is Open Access

Interrater reliability: the kappa statistic

Mary L McHugh (2012)

The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.

0 comments Cited 3843 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Index for rating diagnostic tests

W. J. Youden, Michele Teng, Matthew Taliaferro … (1951)

0 comments Cited 1982 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.

Hanchuan Peng, Fuhui Long, Chris Ding (2005)

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.

0 comments Cited 930 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Duygu Kaya: (View ORCID Profile)

Journal

Title: Concurrency and Computation: Practice and Experience

Abbreviated Title: Concurrency and Computation

Publisher: Wiley

ISSN (Print): 1532-0626

ISSN (Electronic): 1532-0634

Publication date Created: November 30 2022

Publication date (Electronic): August 23 2022

Publication date (Print): November 30 2022

Volume: 34

Issue: 26

Affiliations

[1 ] Department of Electrical and Electronics Engineering Firat University Elazig Turkey

Article

DOI: 10.1002/cpe.7289

SO-VID: c8e81f0a-aa56-4e27-83cc-0099045931b3

License:

http://onlinelibrary.wiley.com/termsAndConditions#vor

History

Data availability:

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.