Table 1_A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing... by Charmayne Mary Lee Hughes (12959972)

“…</p>Conclusion<p>Machine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

Comparison with previous studies. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Dataset characteristics. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Acronym table. by Ji Seung Ryu (16187857)

“…Three datasets were used for the experiment: the male, female, and entire dataset. A cutoff for binary classification was defined as the meaningful as a screening test (<132 g/m² vs. ≥132 g/m², <109 g/m² vs. ≥109 g/m²). …”

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Fairness in Machine Learning: A Review for Statisticians by Xianwen He (22529252)

“…We organize these fairness-enhancing mechanisms into three categories—pre-processing, in-processing, and post-processing—corresponding to different stages of the machine learning lifecycle and varying levels of access to the underlying algorithm. The discussion focuses on fairness in binary classification models using numerical tabular data, which serve as a foundation for addressing fairness in more complex algorithms. …”

Table_10_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_5_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_8_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_6_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_7_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Table_9_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.xlsx by Changho Han (1309563)

Data_Sheet_1_Artificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification.DOCX by Changho Han (1309563)

An adapted Figure [10] demonstrating the process of injury prediction validation using a pattern recognition approach. by Seren Lois Evans (19942603)

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

“…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …”