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

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)

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. …”

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)

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)

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

“…<p>With the widespread application of machine learning algorithms in daily life, it is crucial to mitigate the risk of these algorithms producing socially undesirable outcomes that may disproportionately disadvantage certain groups or individuals based on demographic characteristics such as gender, race, or disabilities. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Analysis of the confusion matrix revealed a critical limitation: although the model correctly identified 785 poisonous mushrooms, it misclassified 313 as edible (false negatives), which represents an unacceptable risk in a practical application.<br> <br>Conclusion<br><br>The study concludes that the habitat variable, used in isolation, is insufficient to create a safe and reliable mushroom toxicity classification model. …”

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG by Suli Yu (14947807)

“…</p>Results<p>A binary classification model was trained based on 25 handpicked features. …”

Table_1_Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.docx by Markus Huber (317962)

“…</p>Objective<p>To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. …”

Predicting childhood obesity using electronic health records and publicly available data by Robert Hammond (3631525)

“…</p><p>Methods and findings</p><p>We trained a variety of machine learning algorithms to perform both binary classification and regression. …”