## 2 Related Works  
Machine learning (ML) is a promising and emerging technique used for the early detection of dementia. ML has been extensively used in the literature for the prediction of cognitive diseases [15]. The use of an ensemble classification model to identify patients with high and low dementia risks was proposed in [16]. The claimed classification accuracy was 94.7% when trained using paralinguistic features only. However, an increase of 2.5% (97.2% accuracy for combined features) in the model's accuracy was reported when both paralinguistic and episodic memory features were used. [PERSON] et al. [17] developed an ensemble of 13 (i.e., SVM with radial basis function, SVM with linear kernel, SVM with polynomial kernel, L1 regularized logistic regression, L2 regularized logistic regression, multilayer perceptron, decision tree, k-NN, random forest, Naive Bayes, liner regression) machine learning models to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease. The authors reported that the ensemble was able to achieve an Area Under Curve (AUC) of 0.88, a specificity of 79.9%, and a sensitivity of 77.7%. [PERSON] et al. [18] proposed a novel approach for dementia diagnosis based on a three-stage deep feature learning and fusion system. [PERSON] et al. [19] proposed a novel feature weighting method based on nearest neighbors called Component Feature Selections (NCFS). This method leverages a feature weighting vector to maximize classification accuracy and was reported to outperform other benchmark techniques. Recently, a shift in research has been observed, and the use of cognitive features for the prediction of Alzheimer's disease (AD) has been reported [20; 21]. [PERSON] et al. [21] used 18 cognitive features for the prediction of dementia. From the results, the authors reported a 0.74 area under the receiver operating characteristic (ROC) curve. [PERSON] et al. [20] used both cognitive and neuroimaging features to predict AD. The authors used only four cognitive features and claimed 81.8% accuracy and an AUC of 0.79 for cognitive features while, for neuroimaging features, 75.7% accuracy and 0.77 AUC. ML models use a series of steps for the identification, training, and testing of algorithms to find the feature(s) of interest for a given dataset. The extracted features play an important role in the performance of the prediction model for dementia. In machine learning, the process of selecting the appropriate features from a dataset to train the model is known as \"feature selection\", which is very important in dataset cleaning [22]. In datasets that contain many features, it is challenging to select the features that are most relevant to the model. As such, removing irrelevant and redundant features from a given dataset improves the overall performance of machine-learning models [23]. Three feature selection approaches are commonly used in the literature, including the filtering approach, the wrapper, and embedded methods [6]. The features are selected based on the multiple statistical test scores and the derived correlation with the target variable. Test scores are based on a correlation coefficient, which defines the statistical relationship between the variables. Other approaches use the correlation coefficient as a feature selector, such as [PERSON]'s correlation, Linear Discriminant Analysis (LDA), Analysis of Variance (ANOVA), and the Chi-Square approach [24]. Furthermore, [PERSON] et al. utilized machine learning to analyze ADNI data, focusing on classifying dementia stages through cognitive and demographic features [25]. Their work, based on the TADPOLE challenge, underscored effective algorithms and highlighted the potential of cognitive tests for non-invasive diagnoses. Their reliance on comprehensive assessments, such as the CDR, supported its application in clinical decision-making and achieved an accuracy rate of 89%. In a similar vein, [PERSON] et al.