Prevalence of normocytic, microcytic and macrocytic by age. حسب Rajan Vohra (10689820)

Prevalence of normocytic, microcytic and macrocytic by age. حسب Rajan Vohra (10689820)

Prevalence of mild, moderate and severe anemia by age. حسب Rajan Vohra (10689820)

Prevalence of mild, moderate and severe anemia by age. حسب Rajan Vohra (10689820)

Prevalence of mild, moderate and severe anemia in percentage by age. حسب Rajan Vohra (10689820)

Presentation_1_Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.pdf حسب Dougho Park (9566450)

"…We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. …"

The data processing and analysis pipeline. حسب Xin Zhang (35492)

Data XGBOOST. حسب Xin Zhang (35492)

"…Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was employed for binary classification (low-moderate vs. high physical activity). …"

Table_1_Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms.DOCX حسب Bei Du (12545461)

"…Purpose<p>To predict the need for cycloplegic assessment, as well as refractive state under cycloplegia, based on non-cycloplegic ocular parameters in school-age children.…"

Model 2: Biomarkers + ACE + Age + Gender. حسب Toheeb Salahudeen (21368040)

Image_1_Development of a new category system for the profile morphology of temporomandibular disorders patients based on cephalograms using cluster analysis.TIFF حسب Rui Zhu (305217)

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

DataSheet_1_Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.docx حسب Tine Geldof (8380125)

"…Secondly, a classification tree algorithm was trained and validated for dividing individual patients into treatment response and non-response groups. …"

Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. حسب Daniel Walke (21680915)

"…In H), we hierarchically clustered (Euclidean distance with average linking) the feature importance resulting from the normalized variance in the partial dependence plots for each trained model. Tree-based algorithms (i.e., Decision Tree, Random Forest, XGBoost, and RUSBoost) are grouped together indicating similar underlying mechanisms for the classification. …"

Dataset selection process and exclusion criteria. حسب Seung Seog Han (4782486)

"…****Severance Dataset A: a total of 10,426 cases (40,331 images; 43 disorders; age mean ± SD = 52.1 ± 18.3, male 45.1%) used for the binary classification (cancer or not). …"

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... حسب Uttam Khatri (12689072)

An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach حسب Sanaa Badr (20628838)

Table_1_Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.docx حسب Lizhao Yan (11774354)

"…Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. …"

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب 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). …"

Table_1_Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional s... حسب Niruwan Turnbull (11506910)

"…Objective<p>To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.…"