We formulated the prediction of dementia as a binary classification problem (dementia, control); therefore, evaluation metrics, such as accuracy, F1-score, balanced accuracy, precision, specificity and sensitivity, were used to measure the performance of the subsets of features. The following evaluation metrics were used:
where n is the number of total classifications per test.
True positives (TP): number of dementia cases that were correctly classified.
False positives (FP): number of healthy subjects incorrectly classified as dementia cases.
True negatives (TN): number of healthy subjects correctly classified.
False negatives (FN): number of dementia cases incorrectly classified as healthy subjects.
Accuracy (%): the proportion of correct classifications among total classifications:
where n is the number of total classifications per test.
Sensitivity (%): The proportion of correctly classified dementia cases.
Specificity (%): The proportion of correctly classified healthy subjects.
Precision: The proportion of subjects classified as dementia cases who have dementia.
F1-score (F-measure) (%): Harmonic mean of precision and sensitivity.
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