Abstract: This work is on decision tree-based classification for
the disbursement of scholarship. Tree-based data mining
classification technique is used in other to determine the generic rule
to be used to disburse the scholarship. The system based on the
defined rules from the tree is able to determine the class (status) to
which an applicant shall belong whether Granted or Not Granted. The
applicants that fall to the class of granted denote a successful
acquirement of scholarship while those in not granted class are
unsuccessful in the scheme. An algorithm that can be used to classify
the applicants based on the rules from tree-based classification was
also developed. The tree-based classification is adopted because of its
efficiency, effectiveness, and easy to comprehend features. The
system was tested with the data of National Information Technology
Development Agency (NITDA) Abuja, a Parastatal of Federal
Ministry of Communication Technology that is mandated to develop
and regulate information technology in Nigeria. The system was
found working according to the specification. It is therefore
recommended for all scholarship disbursement organizations.
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.