NDLI: PROMETHEE-based hybrid feature selection technique for high-dimensional biomedical data: application to Parkinson's disease classification

Content Provider	IET Digital Library
Author	Chaudhuri, A. Sahu, T. P.
Abstract	Biomedical data are being collected for fields like cancer diagnosis and prognosis, brain signals, speech signals, genetic engineering to name a few. These data are very high dimensional these days, which makes it difficult to extract knowledge out of it through machine learning algorithms. In this work, the authors proposed a hybrid feature selection method based on a multi-attribute decision-making method PROMETHEE (preference ranking organisation method for enrichment evaluations) and Jaya optimisation algorithm. Their proposed method works in two phases. In the first phase, five filter methods are applied to get the ranking for each feature of the data set. In the second phase, all the five individual ranks are used as input choices for PROMETHEE which gives us a final rank for all the features. Then the top 3% features are selected for training the machine learning model. This technique is applicable for feature reduction in any high-dimensional biomedical data. Here, they have studied Parkinson's disease data set. The result shows that the proposed method improves the classification accuracy by 13.73% and that too in a minimum amount of time with a minimum number of features. Hence, this method can be used as an essential pre-processing step for high-dimensional biomedical data.
Starting Page	1403
Ending Page	1406
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.2517
Journal	Electronics Letters
Publisher Date	2020-10-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Cancer Data Handling Technique Decision Making Decision-making Method PROMETHEE Diseases Feature Extraction Feature Reduction Feature Selection Filter Method High-dimensional Biomedical Data Hybrid Feature Selection Method Knowledge Acquisition Knowledge Engineering Technique Learning in AI Machine Learning Algorithm Medical Computing Parkinson's Disease Classification Parkinson's Disease Data Pattern Classification Preference Ranking Organisation Method PROMETHEE-based Hybrid Feature Selection Technique
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO

Pilot study: electrical impedance based tissue classification using support vector machine classifier

Classification of healthcare data using hybridised fuzzy and convolutional neural network

Hybrid feature selection method for gene expression analysis

Cancers classification based on deep neural networks and emotional learning approach

A filter feature selection for high-dimensional data

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Performance improvement for machine learning-based cooperative spectrum sensing by feature vector selection

EMD-Based Method for Supervised Classification of Parkinson's Disease Patients Using Balance Control Data

PROMETHEE-based hybrid feature selection technique for high-dimensional biomedical data: application to Parkinson's disease classification

Similar Documents

Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO

Pilot study: electrical impedance based tissue classification using support vector machine classifier

Classification of healthcare data using hybridised fuzzy and convolutional neural network

Hybrid feature selection method for gene expression analysis

Cancers classification based on deep neural networks and emotional learning approach

A filter feature selection for high-dimensional data

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Performance improvement for machine learning-based cooperative spectrum sensing by feature vector selection

EMD-Based Method for Supervised Classification of Parkinson's Disease Patients Using Balance Control Data

PROMETHEE-based hybrid feature selection technique for high-dimensional biomedical data: application to Parkinson's disease classification