<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

Calibration curves for the test set of four models. by Meng Cao (105914)

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ROC curves for the test set of four models. by Meng Cao (105914)

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Performance metrics of the models on the training and test set. by Meng Cao (105914)

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SHAP bar plot. by Meng Cao (105914)

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Display of the web prediction interface. by Meng Cao (105914)

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Sample screening flowchart. by Meng Cao (105914)

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Descriptive statistics for variables. by Meng Cao (105914)

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SHAP summary plot. by Meng Cao (105914)

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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)

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)

Silibinin solubilization: combined effect of co-solvency and inclusion complex formation by Azam Dehghan (18434551)

“…The solubility in PBS-ethanol mixtures followed a log-linear model. SLB solubility in the presence of the ethanol co-solvent and HP-β-CD complexing agent was optimized by adopting a genetic algorithm suggesting the phosphate buffer saline solution supplemented by 6%v/v ethanol and 8 mM HP-β-CD as an optimized medium. …”

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

“…</p>Results and Discussion<p>Experimental evaluation across varied athlete cohorts demonstrates superior performance in risk stratification accuracy, diagnostic plausibility, and model transparency compared to traditional screening algorithms. …”

Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx by Ali Nabavi (21097424)

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”

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

“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx by Çaǧlar Çaǧlayan (12253934)

“…We performed threshold optimization for converting predicted probabilities into binary predictions and identified the cut-off maximizing the sum of sensitivity and specificity.…”

Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx by Yanbo Sun (2202439)

“…Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). …”