Scientific Programming / 2020 / Article / Tab 4

Research Article

Machine Learning for the Preliminary Diagnosis of Dementia

Table 4

Overall performance of the diagnostic models.

AlgorithmFeature selectionAccuracyPrecisionRecallF-measure

Random ForestRelief0.780.800.780.78
Information Gain0.780.790.780.78
Random Forest0.760.770.760.76

AdaBoostRelief0.770.780.770.77
Information Gain0.770.780.770.77
Random Forest0.760.760.760.76

LogitBoostRelief0.800.750.800.76
Information Gain0.780.730.780.74
Random Forest0.760.770.760.74

MLPRelief0.810.750.810.77
Information Gain0.790.730.790.75
Random Forest0.780.760.780.76

Naïve BayesRelief0.790.740.790.75
Information Gain0.810.820.810.81
Random Forest0.770.800.770.78

SVMRelief0.800.740.800.76
Information Gain0.790.730.790.75
Random Forest0.760.740.760.75

Results were obtained after using the feature selection.

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