NDLI: Automatic decision using dirty databases: Application to prostate cancer diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Marin, O.R. Ruiz, D. Soriano, A. Delgado, F.J.
Copyright Year	2010
Description	Author affiliation: University General Hospital of Elche, Spain (Delgado, F.J.) \|\| Bioinspired Engineering and Health Computing Research Group, University of Alicante, P.O. 99 E-03080 Spain (Marin, O.R.; Ruiz, D.; Soriano, A.)
Abstract	Currently, the best way to reduce the mortality of cancer is to detect and treat it in its early stages. Automatic decision support systems, such as automatic diagnosis systems, are very helpful in this task but their performance is constrained by the integrity of the clinical input data. This could be a problem since clinical databases, in which these systems are based on, are commonly built up containing dirty data (empty fields, non-standard or normalized values, etc). This article presents a study of the performance of a clinical decision support system, based on an artificial neural networks, using sets of clean and dirty prostate cancer data. The study shows that is possible to obtain an implementation that allow us to avoid the problems associated to the database's lack of integrity and reach a similar performance using either clean or dirty data.
Starting Page	1162
Ending Page	1165
File Size	626285
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626259
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Prostate cancer Training Databases Classification algorithms Machine learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Health Informatics Signal Processing Biomedical 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 Libarray 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

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

A Novel Ensemble Strategy for Classification of Prostate Cancer Protein Mass Spectra

Convolutional neural networks for mammography mass lesion classification

Intelligent Arrhythmia Detection and Classification Using ICA

Use of radial basis functions in computer-aided diagnosis of prostate cancer

Human gait classification after lower limb fracture using Artificial Neural Networks and principal component analysis

Classification of cardiac arrhythmias using competitive networks

Automatic sleep staging using state machine-controlled decision trees

Automatic decision using dirty databases: Application to prostate cancer diagnosis

Similar Documents

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

A Novel Ensemble Strategy for Classification of Prostate Cancer Protein Mass Spectra

Convolutional neural networks for mammography mass lesion classification

Intelligent Arrhythmia Detection and Classification Using ICA

Use of radial basis functions in computer-aided diagnosis of prostate cancer

Human gait classification after lower limb fracture using Artificial Neural Networks and principal component analysis

Classification of cardiac arrhythmias using competitive networks

Automatic sleep staging using state machine-controlled decision trees

Automatic decision using dirty databases: Application to prostate cancer diagnosis