Comparative Study of ID3/C4.5 Decision tree and Multilayer Perceptron Algorithms for the Prediction of Typhoid Fever
نویسنده
چکیده
Data mining is an essential phase in knowledge discovery in database which is actually used to extract hidden patterns from large databases. Data mining concepts and methods can be applied in various fields like marketing, medicine, real estate, customer relationship management, engineering, web mining, etc. The main objective of this paper is to compare the performance accuracy of Multilayer perceptron (MLP) Artificial Neural Network and ID3 (Iterative Dichotomiser 3), C4.5 (also known as J48) Decision Trees algorithms Weka data mining software in predicting Typhoid fever. The data used is the patient’s dataset collected from a well known Nigerian Hospital. ID3, C4.5 Decision tree and MLP Artificial Neural Network WEKA Data mining software was used for the implementation. The data collected were transformed in a form that is acceptable to the data mining software and it was splitted into two sets: The training dataset and the testing dataset so that it can be imported into the system. The training set was used to enable the system to observe relationships between input data and the resulting outcomes in order to perform the prediction. The testing dataset contains data used to test the performance of the model. This model can be used by medical experts both in the private and public hospitals to make more timely and consistent diagnosis of typhoid fever cases which will reduce death rate in our country. The MLP ANN model exhibits good performance in the prediction of typhoid fever disease in general because of the low values generated in the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Relative Absolute Error (RAE) error performance measures. KeywordsID3, C4.5 , MLP, Decision Tree Artificial Neural Network, Typhoid fever African Journal of Computing & ICT Reference Format: O..O. Adeyemo, T. .O Adeyeye & D. Ogunbiyi (2015). Ccomparative Study of ID3/C4.5 Decision tree and Multilayer Perceptron Algorithms for the Prediction of Typhoid Fever. Afr J. of Comp & ICTs. Vol 8, No. 1. Pp 103-112.
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