A Case Study of Computer-Based Alzheimer Disease Diagnosis at the Premature Stage

Despite its impact on both young and old, early Alzheimer’s disease (AD) diagnosis is difficult. Both developed and developing nations may have to deal with AD and diabetes which affect a huge percentage of the global population, and their detection via machine learning is now receiving a lot of attention. Despite many intelligent algorithms in previous studies, there is a lag in prediction accuracy. The present paper presents a novel feature extraction and selection mechanism by Modified Grey-Level Co-Occurrence Matrix (M-GLCM) and performs a comparative study between Multi-level Singular Value Decomposition (MLSVD), Multi-linear Principal Component Analysis (MLPCA), and Fuzzy Bat Algorithm.