Effectiveness of Artificial Neural Networks in forecasting failure risk for pre-medical students

This research paper evaluates the ability of Artificial Neural Networks (ANN) to predict the performance of the applicants students to Medical Sciences, in order to predict their failure/ risk in their premedical year. Educational institutions in general, consider a variety of factors when making admission decisions. Traditionally, academic researchers have developed several statistical models to predict an applicant's success in the academic programs. An ANN model is designed based on existing academic acceptance criteria for medical college. The Cascade Correlation Learning structure of ANN is used to predict students' final grades in their premedical year. The result of this research shows that the neural network model can predict students' performance even better when the similar characteristics for input data have been maintained.