%0 Journal Article
%T A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies
%A Stanley Ziweritin
%A Barilee Barisi Baridam
%A Ugochi Adaku Okengwu
%J Open Access Library Journal
%V 9
%N 3
%P 1-15
%@ 2333-9721
%D 2022
%I Open Access Library
%R 10.4236/oalib.1108549
%X The decision tree and neural network models are considered as one of the fastest and easy-to-use techniques having the ability to learn from classified data patterns. These models can be employed in detecting result anomalies measurable under normal circumstances on the bases that the student is healthy, had no problem and sat for exams. The existing techniques lack merit and integrity to efficiently detect irregularities found between student continuous assessments and exam scores. The addition of weights and calibrated values aided the learning process and addressed the problem facing the existing methods in operation. This provided an instance of having suitable control over the objective function in overcoming the identified problem. The added calibrated value helped control wrongly classified data patterns and improved the intelligence of the model. In this paper, the K-fold cross-validation test was employed to have a better classification report with the best split. This research was aimed to provide a comparative analysis of neural network and decision tree model for detecting result anomalies. The functionality of both models was used as a measure to check against result anomalies. This resulted in 96% and 91% accuracy with feed-forward multi-layered neural network and decision tree technique.
%K Anomaly Detection
%K Decision Tree
%K Feed-Forward
%K Neural Network
%K Pre-Processing
%U http://www.oalib.com/paper/6771140