Table 5 Machine learning analysis for...

Table 5

Machine learning analysis for dementia prognosis

	ML classifier	Train accuracy (%)	Cross-validation accuracy (%)	Prediction accuracy (%)	Precision (%)	Recall (%)	F1 measure
Generalized linear models
1	Logistic RegressionCV	84.71	84.26	80.73	84.0	81.0	0.82
2	Passive Aggressive	77.37	84.26	78.90	86.0	79.0	0.81
3	Perceptron	82.87	84.83	79.82	70.0	80.0	0.74
4	Ridge ClassifierCV	77.06	84.83	79.82	86.0	80.0	0.82
5	Stochastic Gradient Descent (SGD)	74.01	83.29	75.23	69.0	75.0	0.72
6	XGB	100.0	84.83	86.24	88.0	86.0	0.87
Naïve Bayes models
7	BernoulliNB	55.96	83.29	52.29	60.0	52.0	0.55
8	GaussianNB	77.98	83.29	79.82	88.0	80.0	0.82
Support vector machine models
9	LinearSVC	80.73	83.29	81.65	79.0	82.0	0.77
10	SVC	85.32	84.83	78.90	87.0	79.0	0.81
Discriminant analysis models
11	LDA	77.37	84.83	78.90	88.0	79.0	0.81
12	QDA	84.40	83.29	79.82	86.0	80.0	0.81
Neighbor-based model
13	KNN	86.85	84.78	76.15	85.0	76.0	0.79
Tree-based model
14	Decision Tree	100.0	84.83	84.40	86.0	84.0	0.85
Ensemble-based models
15	AdaBoost	100.0	84.83	85.32	86.0	85.0	0.86
16	Bagging	94.20	91.45	93.67	90.0	88.0	0.89
17	Extra Trees	100.0	84.83	85.32	88.0	85.0	0.86
18	Gradient Boosting	99.70	83.65	86.24	88.0	86.0	0.87
19	Random Forest	97.0	86.76	88.07	90.0	88.0	0.89
Neural network
20	MLP	85.02	84.83	82.57	86.0	83.0	0.84

	ML classifier	Train accuracy (%)	Cross-validation accuracy (%)	Prediction accuracy (%)	Precision (%)	Recall (%)	F1 measure
Generalized linear models
1	Logistic RegressionCV	84.71	84.26	80.73	84.0	81.0	0.82
2	Passive Aggressive	77.37	84.26	78.90	86.0	79.0	0.81
3	Perceptron	82.87	84.83	79.82	70.0	80.0	0.74
4	Ridge ClassifierCV	77.06	84.83	79.82	86.0	80.0	0.82
5	Stochastic Gradient Descent (SGD)	74.01	83.29	75.23	69.0	75.0	0.72
6	XGB	100.0	84.83	86.24	88.0	86.0	0.87
Naïve Bayes models
7	BernoulliNB	55.96	83.29	52.29	60.0	52.0	0.55
8	GaussianNB	77.98	83.29	79.82	88.0	80.0	0.82
Support vector machine models
9	LinearSVC	80.73	83.29	81.65	79.0	82.0	0.77
10	SVC	85.32	84.83	78.90	87.0	79.0	0.81
Discriminant analysis models
11	LDA	77.37	84.83	78.90	88.0	79.0	0.81
12	QDA	84.40	83.29	79.82	86.0	80.0	0.81
Neighbor-based model
13	KNN	86.85	84.78	76.15	85.0	76.0	0.79
Tree-based model
14	Decision Tree	100.0	84.83	84.40	86.0	84.0	0.85
Ensemble-based models
15	AdaBoost	100.0	84.83	85.32	86.0	85.0	0.86
16	Bagging	94.20	91.45	93.67	90.0	88.0	0.89
17	Extra Trees	100.0	84.83	85.32	88.0	85.0	0.86
18	Gradient Boosting	99.70	83.65	86.24	88.0	86.0	0.87
19	Random Forest	97.0	86.76	88.07	90.0	88.0	0.89
Neural network
20	MLP	85.02	84.83	82.57	86.0	83.0	0.84

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