Table 9.

Referenced literature that considered Machine Learning-based Alzheimer disease diagnosis.

Study	Contributions	Algorithm	Dataset	Data Type	Performance Evaluation
[111]	Automatic diagnosis of Alzheimer’s disease and mild cognitive impairment	CNN+SVM	F-FDG PET:PET	Image	Accuracy—74–90%
[112]	Predicting transition from mild cognitive impairment to Alzheimer’s	LR, ARN, DT	1913 privately owned cases	Tabular	Accuracy—(89.52 ± 0.36%), AUC-ROC (92.08 ± 0.12), Sensitivity—(82.11 ± 0.42%) and Positive predictive value (75.26 ± 0.86%)
[113]	Automatic classification of Alzheimer’s	DNN+RF	Tabular	Accuracy—67%