An automated data-driven tool to build artificial neural networks for predictive decision-making

We propose the development of an automated data-driven tool to assist data analysts in building an optimal artificial neural network (ANN) model to solve their domain-specific problems for predictive decision making. The proposed approach combines the strengths of both sequential training methods and multi-hidden-layer learning algorithms to dynamically learn the best-fitted parameters, including learning rate (LR), momentum rate (MR), number of hidden layers (NHL), and number of neurons in each hidden layer (NNHL), for the given set of key input attributes and multiple output nodes. Specifically, the contributions of this work are three-fold: 1) develop the new extended algorithm, i.e., multidimensional parameter learning (MPL), to learn the optimal ANN parameters; 2) provide the user-friendly GUI tool for data analysts to maintain the data manipulations and the tool operations; 3) conduct the experimental case study, i.e., determining the severity level of Alzheimer's patients, to present the superior result (i.e., 95.33%) in terms of prediction accuracy and model complexity by using the learned parameters (i.e., LR = 0.6, MR = 0.8, NHL = 2, NNHL at the 1st layer = 28, and NNHL at the 2nd layer = 24) from the MPL algorithm.