NDLI: Osteoporosis risk prediction using machine learning and conventional methods

Content Provider	IEEE Xplore Digital Library
Author	Sung Kean Kim Tae Keun Yoo Ein Oh Deok Won Kim
Copyright Year	2013
Description	Author affiliation: Dept. of Med. Eng., Yonsei Univ., Seoul, South Korea (Deok Won Kim) \|\| Grad. Program in Biomed. Eng., Yonsei Univ., Seoul, South Korea (Sung Kean Kim) \|\| Dept. of Med., Yonsei Univ., Seoul, South Korea (Tae Keun Yoo; Ein Oh)
Abstract	A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
Starting Page	188
Ending Page	191
File Size	545717
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609469
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Osteoporosis Support vector machines Artificial neural networks Predictive models Radio frequency Accuracy Learning systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine

Vocal Folds Disorder Detection using Pattern Recognition Methods

The predictive power of structural MRI in Autism diagnosis

Statistical Learning Theory and Support Vector Machines

Yarn Properties Prediction Using Support Vector Machines: An Intelligent Reasoning Method

Towards a predictive model for Guillain-Barré syndrome

MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction

Modeling Permeability Prediction Using Extreme Learning Machines

Adverse event prediction in patients with left ventricular assist devices

Osteoporosis risk prediction using machine learning and conventional methods

Similar Documents

Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine

Vocal Folds Disorder Detection using Pattern Recognition Methods

The predictive power of structural MRI in Autism diagnosis

Statistical Learning Theory and Support Vector Machines

Yarn Properties Prediction Using Support Vector Machines: An Intelligent Reasoning Method

Towards a predictive model for Guillain-Barré syndrome

MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction

Modeling Permeability Prediction Using Extreme Learning Machines

Adverse event prediction in patients with left ventricular assist devices

Osteoporosis risk prediction using machine learning and conventional methods