Affiliation:
1. Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai 600073, Tamil Nadu, India
2. Department of Computer Science, Bharath Institute of Higher Education and Research, Selaiyur, Chennai 600073, Tamil Nadu, India
Abstract
Machine learning (ML) and deep learning (DL) techniques can considerably enhance the process of making a precise diagnosis of Alzheimer’s disease (AD). Recently, DL techniques have had considerable success in processing medical data. They still have drawbacks, like large data requirements and a protracted training phase. With this concern, we have developed a novel strategy with the four stages. In the initial stage, the input data is subjected to data imbalance processing, which is crucial for enhancing the accuracy of disease detection. Subsequently, entropy-based, correlation-based, and improved mutual information-based features will be extracted from these pre-processed data. However, the curse of dimensionality will be a serious issue in this work, and hence we have sorted it out via optimization strategy. Particularly, the tunicate updated golden eagle optimization (TUGEO) algorithm is proposed to pick out the optimal features from the extracted features. Finally, the ensemble classifier, which integrates models like CNN, DBN, and improved RNN is modeled to diagnose the diseases by training the selected optimal features from the previous stage. The suggested model achieves the maximum F-measure as 97.67, which is better than the extant methods like [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. The suggested TUGEO-based AD detection is then compared to the traditional models like various performance matrices including accuracy, sensitivity, specificity, and precision.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition