Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare

Table 1. Summary on Machine Learning-Driven for EarlyDetection of Alzheimer's Disease in Healthcare

References	Methodology	Dataset	Performance	Limitations & Future Work
Afzal et al., 2019	Transfer learning + data augmentation on 3D MRI	OASIS	98.41% (single view), 95.11% (3D view)	Class imbalance in dataset; need for balanced multiclass classification of AD stages
Ahmed et al., 2019	CNN ensemble on TVPs of left/right hippocampus	GARD	90.05% accuracy	Focused only on hippocampus; small dataset; overfitting still a concern
Silva et al., 2019	Deep feature extraction + classical ML classifiers (RF, SVM, K-NN)	MIRIAD	RF: 88.32%, SVM: 96.07%, K-NN: 87.45%	Limited region of brain (30 slices); classification only between AD vs. HC
Altaf et al., 2018	Hybrid of clinical + texture features; BoVW model	ADNI	Binary: 98.4%, Multi-class: 79.8%	Moderate performance in multi-class classification; relies on handcrafted features
Mahyoub et al., 2018	ML classifiers on lifestyle, demography, and medical history	Custom tabular dataset	Sensitivity: 0.741, Specificity: 0.515 (test)	Poor generalization; low precision; limited data modalities
Padole et al., 2018	Graph CNN using resting-state fMRI	ADNI	92.44% accuracy	Focused only on fMRI; computationally intensive graph construction