Fig. 3
The general scheme used during ML classification involves several steps. Initially, patients are assigned to one of three categories: C (control), MCI (mild cognitive impairment), D (dementia). Then the discrimination is performed either between patients labeled as C and MCI, C and D or C and MCI+D, based on their medical records. Next, missing values in the data are handled with the KNN imputation algorithm. Subsequently, the most significant features are derived from the dataset using the univariate statistical test. To address data imbalance, the SMOTE technique is applied. Such prepared data is then fed into a ML classifier. To optimize the results, the best hyperparameters of the model are selected using Grid Search combined with k-fold Cross Validation.
