@article{(Open Science Index):https://publications.waset.org/pdf/10002071,
	  title     = {Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus},
	  author    = {J. K. Alhassan and  B. Attah and  S. Misra},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Health and Medical Engineering},
	  volume    = {9},
	  number    = {9},
	  year      = {2015},
	  pages     = {669 - 672},
	  ee        = {https://publications.waset.org/pdf/10002071},
	  url   	= {https://publications.waset.org/vol/105},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 105, 2015},
	}