Predicting the Cognitive Ability of Young Women Using a New Feature Selection Algorithm

This study aimed to investigate the relationship between several biomarkers and individuals ’ cognitive ability using various machine learning methods. A total of 144 young women aged between 18 and 24 years old were recruited into the study. Cognitive performance was assessed using a standard questionnaire. A panel of biochemical, hematological, inflammatory, and oxidative stress bioma rkers in serum and urine was measured for all participants. A novel combination of feature selection and feature scoring techniques within a hierarchical ensemble structure has been proposed to identify the most effective features in recognizing the importance of various biomarker signatures in cogn itive abilities classification. Multiple feature selection methods were employed in conjunction with different classifiers to construct this model. In this manner, using three filter methods, the scores of each feature were considered. The combination of high-scoring features for each filter method was stored as the primary feature subset. A high-accuracy feature subset was selected by using a wrapper method. The collection of highly scored features from each filter method formed the primary feature subset. A wrapper method was also employed to select a feature subset with high accuracy. To en sure robustness and minimize random variations in the feature subset search process, a repeative tenfold cross-validation was conducted. The most frequently recurring features were determined. This iterati...
Source: Journal of Molecular Neuroscience - Category: Neuroscience Source Type: research