Journal: PeerJ

Article Title: A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment

doi: 10.7717/peerj.18490

Figure Lengend Snippet: (A) Selection of participants data corresponding to healthy controls (HC) and participants who transitioned to MCI (uHC) in a period lower or equal than 5 years for ADNI and OASIS-3 datasets. These are imbalanced datasets as shown by the integer values indicating the number of samples in each group. A manually balanced cohort was extracted from ADNI dataset to be used within MCL app analysis. (B) Each data was optionally pre-processed using two different data correction procedures: residual and z -score harmonization. (C) Uncorrected and processed ADNI data undergone statistical analysis using SPSS software for assessing significant features. (D) The MATLAB’s Classification Learner (MCL) app was utilized for evaluating a wide range of feature selection and classification methods, using an ADNI-balanced cohort. The MCL app includes many popular classifiers, such as Gaussian/Kernel Naïve Bayes (GNB/KNB), support vector machine (SVM), and artificial neural networks (ANN). Overall, we performed a preliminary selection of “best” classifiers and features from the MCL app and SPSS analysis. (E) Further evaluation of selected features and classification methods was performed through our proposed customized pipeline, implementing nested cross-validation (CV) and Bayesian optimization within a Monte Carlo replication framework. This last analysis was performed for both ADNI and OASIS-3 imbalanced datasets.* MATLAB symbol derived from: https://www.mathworks.com/?s_tid=gn_logo .

Article Snippet: Distance function: “cityblock”, “chebychev”, “correlation”, “cosine”, “euclidean”, “hamming”, “jaccard”, “mahalanobis”, “minkowski”, “seuclidean”, or “spearman”. (3) SVM ( https://uk.mathworks.com/help/stats/fitcsvm.html ) Kernel function: “gaussian”, “rbf”, “linear”, or “polynomial”.

Techniques: Selection, Software, Plasmid Preparation, Biomarker Discovery, Derivative Assay

Journal: PeerJ

Article Title: A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment

doi: 10.7717/peerj.18490

Figure Lengend Snippet: Selection frequency as top performer for each classification method under different feature selection criteria. The results for the classification methods are presented across the rows, whereas the columns present the outcome for the different feature selection strategies.

Article Snippet: Distance function: “cityblock”, “chebychev”, “correlation”, “cosine”, “euclidean”, “hamming”, “jaccard”, “mahalanobis”, “minkowski”, “seuclidean”, or “spearman”. (3) SVM ( https://uk.mathworks.com/help/stats/fitcsvm.html ) Kernel function: “gaussian”, “rbf”, “linear”, or “polynomial”.

Techniques: Selection