ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…Second, EEG generates a rather noisy image of brain activity, which results in a poor classification performance. …”

Receiver operating curves for NLP classification. by Charlene Jennifer Ong (8997278)

Architecture of the proposed deep learning model for hip disease screening. by Jung-Wee Park (10689240)

Imaging parameters. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”

Data_Sheet_1_Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning.pdf by Shengqi Yang (9269216)

“…Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca²⁺ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Data_Sheet_1_Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.docx by Xueyan Zhang (277449)

Image1_Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.TIF by Álmos Orosz (12828104)

Supplementary Material for: Utilizing Deep Learning to Identify Electron-Dense Deposits in Renal Biopsy Electron Microscopy Images by figshare admin karger (2628495)

“…To evaluate the model's classification capability, we created a binary classification model to identify the presence of deposits in EM images. …”

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”

Participants’ demographic characteristics. by Reihaneh Hassanzadeh (11986041)

“…We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). …”

Supplementary Material for: Detecting atrial fibrillation by artificial intelligence enabled neuroimaging examination. by figshare admin karger (2628495)

“…Machine learning is gaining greater traction for clinical decision making and may help facilitate the detection of undiagnosed AF when applied to magnetic resonance imaging (MRI). We hypothesise that machine learning algorithm increases the accurate classification of MRIs of stroke patients into those due to AF vs large artery atherosclerosis. …”

DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”

DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx by Francesca Piludu (10706391)

“…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”