SHAP bar plot. by Meng Cao (105914)

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

Sample screening flowchart. by Meng Cao (105914)

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

Descriptive statistics for variables. by Meng Cao (105914)

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

SHAP summary plot. by Meng Cao (105914)

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

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

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

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

“…According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration.…”

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”

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

“…<br>The consistency of the results across different kernels demonstrates that the information contained in the habitat, by itself, leads to a very simple optimal decision rule (mostly the prediction of the most frequent class per habitat), which cannot be improved solely by model adjustments. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…This measurement was conducted by altering the number of desired corners from 2 to 20. The optimal number that produced the most favourable results in the preliminary differentiation was determined to be 5, which was subsequently selected and utilized for this measurement.…”

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

“…Objective<p>Nasopharyngeal carcinoma (NPC) patients frequently present with distant metastasis (DM), which is typically associated with poor prognosis. …”

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 applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. 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). …”