Intelligence model for Alzheimer’s disease detection with optimal trained deep hybrid model

Abstract

Alzheimer’s disease (AD), a neurodegenerative disorder, is the most common cause of dementia and continuing cognitive deficits. Since there are more cases each year, AD has grown to be a serious social and public health issue. Early detection of the diagnosis of Alzheimer’s and dementia disease is crucial, as is giving them the right care. The importance of early AD diagnosis has recently received a lot of attention. The patient cannot receive a timely diagnosis since the present methods of diagnosing AD take so long and are so expensive. That’s why we created a brand-new AD detection method that has four steps of operation: pre-processing, feature extraction, feature selection, and AD detection. During the pre-processing stage, the input data is pre-processed using an improved data normalization method. Following the pre-processing, these pre-processed data will go through a feature extraction procedure where features including statistical, enhanced entropy-based and mutual information-based features will be extracted. The appropriate features will be chosen from these extracted characteristics using the enhanced Chi-square technique. Based on the selected features, a hybrid model will be used in this study to detect AD. This hybrid model combines classifiers like Long Short Term Memory (LSTM) and Deep Maxout neural networks, and the weight parameters of LSTM and Deep Maxout will be optimized by the Self Updated Shuffled Shepherd Optimization Algorithm (SUSSOA). Our Proposed SUSSOA-based method’s statistical analysis of best values such as 57%, 53%, 28%, 25%, and 21% is higher than the other models like SSO, BMO, HGS, BRO, BES, and ISSO respectively.