Analysis of a Population of Diabetic Patients Databases with Classifiers
Data mining can be called as a technique to extract
information from data. It is the process of obtaining hidden
information and then turning it into qualified knowledge by statistical
and artificial intelligence technique. One of its application areas is
medical area to form decision support systems for diagnosis just by
inventing meaningful information from given medical data. In this
study a decision support system for diagnosis of illness that make use
of data mining and three different artificial intelligence classifier
algorithms namely Multilayer Perceptron, Naive Bayes Classifier and
J.48. Pima Indian dataset of UCI Machine Learning Repository was
used. This dataset includes urinary and blood test results of 768
patients. These test results consist of 8 different feature vectors.
Obtained classifying results were compared with the previous studies.
The suggestions for future studies were presented.
<p>[1] M. Khajehei, F. Etemady, "Data Mining and Medical Research Studies,"
cimsim, pp.119-122, 2010 Second International Conference on
Computational Intelligence, Modelling and Simulation, 2010
[2] T. Jayalakshmi, A. Santhakumaran, , "A Novel Classification Method
for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks,"
Data Storage and Data Engineering (DSDE), 2010 International
Conference on , vol., no., pp.159-163, 9-10 Feb. 2010
[3] E.I.Mohamed, R.Linderm, G.Perriello,N.Daniele, S.J.Poppl, &
A.DeLorenzo. “Predicting type 2 diabetes using an electronic nose-based
artificial neural network analysis,” Diabetes nutrition metabolism,
15(4),215–221.202.
[4] J.C.Pickup, G. Williams, (Eds.), Textbook of diabetes, Blackwell
Science, Oxford.
[5] Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
[6] WEKA, by university of Waikato,
http://www.cs.waikato.ac.nz/ml/weka/
[7] P. Yasodha, M. Kannan, “Analysis of a Population of Diabetic Patient
Databases in Weka Tool”, International Journal of Scientific &
Engineering Research Volume 2, Issue 5, May-2011.
[8] Jiang Ming-Yan; Chen Zhi-Jian; , "Diabetes expert system," Intelligent
Processing Systems, 1997. ICIPS '97. 1997 IEEE International
Conference on , vol.2, no., pp.1076-1077 vol.2, 28-31 Oct 1997
[9] Jianchao Han; Rodriguez, J.C.; Beheshti, M.; , "Diabetes Data Analysis
and Prediction Model Discovery Using RapidMiner," Future Generation
Communication and Networking, 2008. FGCN '08. Second International
Conference on , vol.3, no., pp.96-99, 13-15 Dec. 2008
[10] B.M Patil, R.C. Joshi, D. Toshniwal, "Association Rule for
Classification of Type-2 Diabetic Patients," Machine Learning and
Computing (ICMLC), 2010 Second International Conference on , vol.,
no., pp.330-334, 9-11 Feb. 2010
[11] A. A. Aljarullah, “Decision tree discovery for the diagnosis of type-II
diabetes”, International Conference on Innovations in Information
Technology, pp. 303-307, 2011.
[12] R. Arora, Suman, “Compatative analysis of classification algorithms on
different dataset using WEKA”, International Journal of Computer
Application, Vol. 54, no:13,pp.21-25, 2012.
[13] D.Marquardt, “An Algorithm for Least Squares Estimation of Non-
Linear Parameter”, J. Soc. Ind. Appl. Math., pp. 1963.
[14] L.Fausett, “Fundamentals of Neural Networks Architecture.Algorithms
and Applications”, Pearson Prentice Hall, USA, 1994.
[15] M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail “The
Use of a Neural Network Technique for the Prediction of Water Quality
Parameters of Axios River in Northern Greece”, Journal 0f Operational
Research, Springer-Verlag, Jan 2005, pp. 115-125.
[16] L.Khuan, N.Hamzah and R Jailani, “Water Quality Prediction Using LSSVM
with Particle Swarm Optimization”,Second International
Workshop on Knowledge Discovery and Data Mining, China, 2009, pp.
900-904.
[17] A. K. Sharma, “ A comparative Study of Classification Algorithms for
Spam Email Data Analysis”, International Journal on Computer Science
and Engineering (IJCSE), Vol. 3, No:5,pp. 1890-1895, 2011.
[18] J. Kim, D. X. Le and G. R. Thoma, “ Naive Bayes Classifier for
Extracting Bibliographic Information from Biomedical Online Articles,”
DMIN , pp.373-378, 2008.</p>
<p>[1] M. Khajehei, F. Etemady, "Data Mining and Medical Research Studies,"
cimsim, pp.119-122, 2010 Second International Conference on
Computational Intelligence, Modelling and Simulation, 2010
[2] T. Jayalakshmi, A. Santhakumaran, , "A Novel Classification Method
for Diagnosis of Diabetes Mellitus Using Artificial Neural Networks,"
Data Storage and Data Engineering (DSDE), 2010 International
Conference on , vol., no., pp.159-163, 9-10 Feb. 2010
[3] E.I.Mohamed, R.Linderm, G.Perriello,N.Daniele, S.J.Poppl, &
A.DeLorenzo. “Predicting type 2 diabetes using an electronic nose-based
artificial neural network analysis,” Diabetes nutrition metabolism,
15(4),215–221.202.
[4] J.C.Pickup, G. Williams, (Eds.), Textbook of diabetes, Blackwell
Science, Oxford.
[5] Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
[6] WEKA, by university of Waikato,
http://www.cs.waikato.ac.nz/ml/weka/
[7] P. Yasodha, M. Kannan, “Analysis of a Population of Diabetic Patient
Databases in Weka Tool”, International Journal of Scientific &
Engineering Research Volume 2, Issue 5, May-2011.
[8] Jiang Ming-Yan; Chen Zhi-Jian; , "Diabetes expert system," Intelligent
Processing Systems, 1997. ICIPS '97. 1997 IEEE International
Conference on , vol.2, no., pp.1076-1077 vol.2, 28-31 Oct 1997
[9] Jianchao Han; Rodriguez, J.C.; Beheshti, M.; , "Diabetes Data Analysis
and Prediction Model Discovery Using RapidMiner," Future Generation
Communication and Networking, 2008. FGCN '08. Second International
Conference on , vol.3, no., pp.96-99, 13-15 Dec. 2008
[10] B.M Patil, R.C. Joshi, D. Toshniwal, "Association Rule for
Classification of Type-2 Diabetic Patients," Machine Learning and
Computing (ICMLC), 2010 Second International Conference on , vol.,
no., pp.330-334, 9-11 Feb. 2010
[11] A. A. Aljarullah, “Decision tree discovery for the diagnosis of type-II
diabetes”, International Conference on Innovations in Information
Technology, pp. 303-307, 2011.
[12] R. Arora, Suman, “Compatative analysis of classification algorithms on
different dataset using WEKA”, International Journal of Computer
Application, Vol. 54, no:13,pp.21-25, 2012.
[13] D.Marquardt, “An Algorithm for Least Squares Estimation of Non-
Linear Parameter”, J. Soc. Ind. Appl. Math., pp. 1963.
[14] L.Fausett, “Fundamentals of Neural Networks Architecture.Algorithms
and Applications”, Pearson Prentice Hall, USA, 1994.
[15] M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail “The
Use of a Neural Network Technique for the Prediction of Water Quality
Parameters of Axios River in Northern Greece”, Journal 0f Operational
Research, Springer-Verlag, Jan 2005, pp. 115-125.
[16] L.Khuan, N.Hamzah and R Jailani, “Water Quality Prediction Using LSSVM
with Particle Swarm Optimization”,Second International
Workshop on Knowledge Discovery and Data Mining, China, 2009, pp.
900-904.
[17] A. K. Sharma, “ A comparative Study of Classification Algorithms for
Spam Email Data Analysis”, International Journal on Computer Science
and Engineering (IJCSE), Vol. 3, No:5,pp. 1890-1895, 2011.
[18] J. Kim, D. X. Le and G. R. Thoma, “ Naive Bayes Classifier for
Extracting Bibliographic Information from Biomedical Online Articles,”
DMIN , pp.373-378, 2008.</p>
@article{"International Journal of Medical, Medicine and Health Sciences:62448", author = "Murat Koklu and Yavuz Unal", title = "Analysis of a Population of Diabetic Patients Databases with Classifiers", abstract = "Data mining can be called as a technique to extract
information from data. It is the process of obtaining hidden
information and then turning it into qualified knowledge by statistical
and artificial intelligence technique. One of its application areas is
medical area to form decision support systems for diagnosis just by
inventing meaningful information from given medical data. In this
study a decision support system for diagnosis of illness that make use
of data mining and three different artificial intelligence classifier
algorithms namely Multilayer Perceptron, Naive Bayes Classifier and
J.48. Pima Indian dataset of UCI Machine Learning Repository was
used. This dataset includes urinary and blood test results of 768
patients. These test results consist of 8 different feature vectors.
Obtained classifying results were compared with the previous studies.
The suggestions for future studies were presented.
", keywords = "Artificial Intelligence, Classifiers, Data Mining,
Diabetic Patients.", volume = "7", number = "8", pages = "497-3", }
