Screening dementia and predicting high dementia risk groups using machine learning

Table 1 Summary of studies

Ref.	Data	Features	Models/algoritms	Results
Bansal et al[2]	Total of 416 subjects in cross-sectional data and 373 records in longitudinal data	Age, sex, education, socioeconomic status, mini-mental state examination, clinical dementia rating, atlas scaling factor, estimated total intracranial volume, and normalized whole-brain volume	J48, naive Bayes, random forest, multilayer perceptron	Classification accuracy; J48: 99.52%; Naive Bayes: 99.28%; Random forest: 92.55%; Multilayer perceptron: 96.88%
Bhagyashree et al[3]	Total of 466 men and women, health and ageing, in South India	Consortium to establish a registry for Alzheimer’s disease, community screening instrument fordementia	Jrip, naive Bayes, random forest and J48, synthetic minority oversampling technique	Sensitivity; Word list recall (WLR) score lower than the population mean: 92.5%; cog-score/verbal fluency/WLR score lower than 0.5 SD lower than population mean: 85.1%
Zhu et al[4]	Total of 5272 patients were analyzed. Normal cognition, mild cognitive impairment, very mild dementia	History of cognitive status, objective assessments including the clinical dementia rating, cognitive abilities screening instrument, and montreal cognitive assessment	Random forest, AdaBoost, LogitBoost, neural network, naive Bayes, and support vector machine (SVM)	Overall performance of the diagnostic models; Overall accuracy; Random forest: 0.86; AdaBoost: 0.83; LogitBoost: 0.81; Multilayer perceptron: 0.87; Naive Bayes: 0.87; SVM: 0.87
Jammeh et al[5]	Total of 26483 patients aged > 65 yr (National Health Service data)	Total of 15469 read codes, of which 4301 were diagnosis codes, 5028 process of care codes, and 6101 medication codes	SVM, naive Bayes, random forest, logistic regression	Naive Bayes classifier gave the best performance with a sensitivity and specificity of 84.47% and 86.67%; The area under the curve naive Bayes: 0.869

WLR: Word list recall; SVM: Support vector machine.