Application of modeling techniques to diabetes diagnosis

In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN...

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Main Authors:	Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Shafie, Amir Akramin
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	R Medicine (General)
Online Access:	http://irep.iium.edu.my/1783/ http://irep.iium.edu.my/1783/ http://irep.iium.edu.my/1783/1/Application_of_Modeling_Techniques_to_Diabetes.pdf

id	iium-1783
recordtype	eprints
spelling	iium-17832011-10-18T08:33:27Z http://irep.iium.edu.my/1783/ Application of modeling techniques to diabetes diagnosis Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin R Medicine (General) In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN based parametric modeling approaches have been suggested. Application of complex data normalization technique converts the real valued input data to complex valued data (CVD) by the process of phase encoding over unity magnitude. CVNN learn the relationship between the input and output phase encoded data during training and the coefficients of Complex-valued autoregressive (CAR) model can be extracted from the complex-valued weights and coefficients of the trained network. Classification of the obtained CAR or RVAR model coefficients results in required distinct classes for diagnosis purpose. Similar operations can be performed for real-valued autoregressive technique except for CVD normalization. The effect of data normalization techniques, activation functions, learning rate, number of neurons in the hidden layer and the number of epoch using the suggested techniques on PIMA INDIA diabetes dataset have been evaluated in this paper. Results obtained compares favorably with earlier reported results. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/1783/1/Application_of_Modeling_Techniques_to_Diabetes.pdf Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka and Shafie, Amir Akramin (2010) Application of modeling techniques to diabetes diagnosis. In: 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), 30 November - 2 December 2010, Kuala Lumpur, malaysia. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5742227&tag=1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	R Medicine (General)
spellingShingle	R Medicine (General) Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin Application of modeling techniques to diabetes diagnosis
description	In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN based parametric modeling approaches have been suggested. Application of complex data normalization technique converts the real valued input data to complex valued data (CVD) by the process of phase encoding over unity magnitude. CVNN learn the relationship between the input and output phase encoded data during training and the coefficients of Complex-valued autoregressive (CAR) model can be extracted from the complex-valued weights and coefficients of the trained network. Classification of the obtained CAR or RVAR model coefficients results in required distinct classes for diagnosis purpose. Similar operations can be performed for real-valued autoregressive technique except for CVD normalization. The effect of data normalization techniques, activation functions, learning rate, number of neurons in the hidden layer and the number of epoch using the suggested techniques on PIMA INDIA diabetes dataset have been evaluated in this paper. Results obtained compares favorably with earlier reported results.
format	Conference or Workshop Item
author	Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin
author_facet	Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin
author_sort	Aibinu, Abiodun Musa
title	Application of modeling techniques to diabetes diagnosis
title_short	Application of modeling techniques to diabetes diagnosis
title_full	Application of modeling techniques to diabetes diagnosis
title_fullStr	Application of modeling techniques to diabetes diagnosis
title_full_unstemmed	Application of modeling techniques to diabetes diagnosis
title_sort	application of modeling techniques to diabetes diagnosis
publishDate	2010
url	http://irep.iium.edu.my/1783/ http://irep.iium.edu.my/1783/ http://irep.iium.edu.my/1783/1/Application_of_Modeling_Techniques_to_Diabetes.pdf
first_indexed	2023-09-18T20:09:17Z
last_indexed	2023-09-18T20:09:17Z
_version_	1777407368966963200

Application of modeling techniques to diabetes diagnosis

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