Design of adaptive hybrid classification model using genetic-based linear adaptive skipping training (GLAST) algorithm for health-care dataset

Abstract

Machine-learning techniques are being used in the health-care industry to improve care delivery at a lower cost and in less time. Artificial Neural Network (ANN) is well-known machine-learning techniques for its diagnostic applications, but it is also increasingly being utilized to guide health-care management decisions. At the same time, in the healthcare industry, ANN has made significant progress in solving a variety of real-world classification problems that range from linear to non-linear and also from simple to complex. In this research work, an Adaptive Hybrid Classification Model named as Genetic-based Linear Adaptive Skipping Training (GLAST) Algorithm has been proposed for the health-care dataset. It has been designed as two-stage process. In first stage, Genetic Algorithm (GA) is adapted to optimize the Learning rate. After optimizing the Learning rate, the optimal Learning rate has been set to the ANN model is ŋ = 1e−4. In the second stage, The training process is carried out using the Linear Adaptive Skipping Training (LAST) algorithm, which reduces the total training time and thus increases the training speed. As a result, the highlighted characteristics of LAST have been integrated with GA to accomplish rapid classification and enhance computational efficiency. On 8 different health-care datasets extracted from the UCI Repository, the proposed GLAST algorithm outperforms both the BPN and LAST algorithms in terms of accuracy and training time, according to simulation results. The result analyses have proved that the efficiency of this proposed GLAST Algorithm outperforms over the existing techniques such as BPN and LAST in terms of accuracy and training time. On various datasets, experimental results show that GLAST improves accuracy from 4 to 17% over BPN training algorithm and reduces overall training time from 10 to 57% over BPN training algorithm.