Variants of history models used for training the general model using data of 40 patients. حسب Tomasz Gutowski (17200042)

Simulated trajectories from real data, optimal schedule, and nearest optimal schedule. حسب Samuel Christensen (9922554)

Comparative analysis of model performance indicators and diagnostic visualizations for full-featured and simplified models across ten algorithms. حسب Aref Andishgar (18155608)

The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

History of the total number of births to U.S. residents per year and the adoption growth of the <i>2003 Revised Birth Certificate</i>. حسب Karl W. Schulz (14309461)

IRBMO vs. meta-heuristic algorithms boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

IRBMO vs. feature selection algorithm boxplot. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Data_Sheet_1_An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening.pdf حسب Luuk A. van Duuren (12001607)

The proposed framework model–HMLFSM. حسب Murad Al-Rajab (10661360)

"…Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. …"

Comparison of the proposed model, with others in the literature. حسب Murad Al-Rajab (10661360)

The number of relevant genes (features) selected by the proposed model on each phase. حسب Murad Al-Rajab (10661360)

Predictive model-building process. حسب Mukhtar Ijaiya (18935122)

"…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …"

Comparison of models performance metrics. حسب Mukhtar Ijaiya (18935122)

"…Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. …"

Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genom... حسب Medhat A. Tawfeek (22522087)

Comparative performance metrics of ML models. حسب Rajeev Bopche (20208606)

"…By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. …"

Parameter Settings for the Proposed Hybrid Model. The Particle Swarm Optimization (PSO) parameters (swarm size, iterations, inertia weight, cognitive and social coefficient) were e... حسب Medhat A. Tawfeek (22522087)

Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalit... حسب Medhat A. Tawfeek (22522087)

Consort diagram for the study. حسب Nicola Lazzarini (3528197)

الموضوعات:

Performance confidence intervals. حسب Nicola Lazzarini (3528197)

الموضوعات:

Baseline characteristics of patients in the study cohort. حسب Nicola Lazzarini (3528197)

الموضوعات: