Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus
Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
[1] G. Karegowda, A. S. Manjunath, and M. A. Jayaram, “Application of
Genetic Algorith Optimized Neural Network Connection Weighs For
Medical Diagnosis of Pima Indians Diabetes,” International Journal on
Soft Computing (IJSC), Vol. 2 No. 2. 2011, pp. 10-15.
[2] P. Saurabh, “Mining Educational Data to Reduce Dropout Rates of
Engineering Student”, International Journal of Information Engineering
and Electronic Business, 2012. Downloaded from http://www.mecspress.
org on Sept., 2014.
[3] Y. Radhika, and M. Shashi, “Atmoshere Temperature Prediction using
Support Vector Machines,” International Journal of Computer Theory
and Engineering, Vol. 1 No.1, 2009, pp. 55 – 57.
[4] Z. Bobby, World Health Organization Report on Nigerian Diabetes,
Downloaded from http://sunnewsonline.com/new/3-9m-nigeriansdiabetic-
says-report/ on 24th July, 2015
[5] J. Maroco, D. Silva, M. Guerreiro, A. de Mendonça, I. Santana.
“Prediction of dementia patients: A comparative approach using
parametric vs. non parametric classifiers,” in Proc. XIX Congresso
Anual da Sociedade Portuguesa de Estatistica, Portuguese, 2011.
[6] Kurt, M. Ture, A.T. Kurum. “Comparing performances of logistic
regression, classification and regression tree, and neural networks for
predicting coronary artery disease”. Expert Syst Appl, vol. 34, pp. 366-
374, 2008.
[7] Endo, T. Shibata, H. Tanaka. “Comparison of seven algorithms to
predict breast cancer survival”. Biomedical Soft Computing and Human
Sciences, vol. 13, pp. 11-16, 2008.
[8] M. Ture, I Kurt, A.T. Kurum, K. Ozdamar. “Comparing classification
techniques for predicting essential hypertension”. Expert Syst Appl, vol.
29, pp. 583-588, 2005.
[9] Morteza, M. Nakhjavani, F. Asgarani, F.L.F Carvalho, R. Karimi, A.
Esteghamati. “Inconsistency in albuminuria predictors in type 2
diabetes: A comparison between neural network and conditional logistic
regression”. Translational Research, vol. 161, pp. 397-405, 2013.
[10] X. Meng, Y. Huang, D. Rao, Q. Zhang, Q. Liu. “Comparison of three
data mining models for predicting diabetes or preetes by risk factors”.
Kaohsiung J Med Sci, vol. 29, pp. 93-99, 2013.
[11] M. Ture, Z. Akturk, I. Kurt, N. Dagdeviren. “The effect of health status,
nutrition, and some other factors on low school performance using
induction technique”. Trakya Univ Tip Fak Derg, vol. 23, pp. 28-38,
2006.
[1] G. Karegowda, A. S. Manjunath, and M. A. Jayaram, “Application of
Genetic Algorith Optimized Neural Network Connection Weighs For
Medical Diagnosis of Pima Indians Diabetes,” International Journal on
Soft Computing (IJSC), Vol. 2 No. 2. 2011, pp. 10-15.
[2] P. Saurabh, “Mining Educational Data to Reduce Dropout Rates of
Engineering Student”, International Journal of Information Engineering
and Electronic Business, 2012. Downloaded from http://www.mecspress.
org on Sept., 2014.
[3] Y. Radhika, and M. Shashi, “Atmoshere Temperature Prediction using
Support Vector Machines,” International Journal of Computer Theory
and Engineering, Vol. 1 No.1, 2009, pp. 55 – 57.
[4] Z. Bobby, World Health Organization Report on Nigerian Diabetes,
Downloaded from http://sunnewsonline.com/new/3-9m-nigeriansdiabetic-
says-report/ on 24th July, 2015
[5] J. Maroco, D. Silva, M. Guerreiro, A. de Mendonça, I. Santana.
“Prediction of dementia patients: A comparative approach using
parametric vs. non parametric classifiers,” in Proc. XIX Congresso
Anual da Sociedade Portuguesa de Estatistica, Portuguese, 2011.
[6] Kurt, M. Ture, A.T. Kurum. “Comparing performances of logistic
regression, classification and regression tree, and neural networks for
predicting coronary artery disease”. Expert Syst Appl, vol. 34, pp. 366-
374, 2008.
[7] Endo, T. Shibata, H. Tanaka. “Comparison of seven algorithms to
predict breast cancer survival”. Biomedical Soft Computing and Human
Sciences, vol. 13, pp. 11-16, 2008.
[8] M. Ture, I Kurt, A.T. Kurum, K. Ozdamar. “Comparing classification
techniques for predicting essential hypertension”. Expert Syst Appl, vol.
29, pp. 583-588, 2005.
[9] Morteza, M. Nakhjavani, F. Asgarani, F.L.F Carvalho, R. Karimi, A.
Esteghamati. “Inconsistency in albuminuria predictors in type 2
diabetes: A comparison between neural network and conditional logistic
regression”. Translational Research, vol. 161, pp. 397-405, 2013.
[10] X. Meng, Y. Huang, D. Rao, Q. Zhang, Q. Liu. “Comparison of three
data mining models for predicting diabetes or preetes by risk factors”.
Kaohsiung J Med Sci, vol. 29, pp. 93-99, 2013.
[11] M. Ture, Z. Akturk, I. Kurt, N. Dagdeviren. “The effect of health status,
nutrition, and some other factors on low school performance using
induction technique”. Trakya Univ Tip Fak Derg, vol. 23, pp. 28-38,
2006.
@article{"International Journal of Medical, Medicine and Health Sciences:70574", author = "J. K. Alhassan and B. Attah and S. Misra", title = "Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus", abstract = "Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.", keywords = "Artificial neural network, classification, decision
tree, diabetes mellitus.", volume = "9", number = "9", pages = "669-4", }