№ files_lp_4_process_2_60432
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Supplementary material providing detailed SHAP-based visualizations of how individual lipid-related features contribute to predicted knee pain risk across multiple machine learning models.
Year: 2026
Region / City: Not specified
Topic: Machine Learning, Health, Knee Pain Risk Prediction
Document Type: Supplementary Material
Institution: Not specified
Authors: Not specified
Target Audience: Researchers in biomedical data science and predictive modeling
Models Used: Deep Neural Network (DNN), Stacked Ensemble, GBM, GLM, Random Forest
Features Analyzed: Lipid biomarkers (LDL-C, HDL-C, triglycerides, cholesterol), LAP, CTI, TyG, TyG-BMI
Data Visualization Methods: SHAP force plots, SHAP decision plots, SHAP dependence plots
Purpose: Explain individual-level model predictions and feature contributions
Price: 8 / 10 USD
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Mapping Development and Predicting the Future in Bowdoinham, Maine: A Temporal Analysis of Development and Subsequent Predictions for the Future
Year: 2020
Region / City: Bowdoinham, Maine
Subject: Development, Urban Planning
Document Type: Research Paper
Organization / Institution: Bowdoinham Town Government
Author: Rodger Heidgerken
Target Audience: Local Government Officials, Urban Planners
Period of Validity: 2011-2024
Approval Date: N/A
Modification Date: N/A
Predictions for 2026: How the K Shaped Economy Will Impact Small Businesses and Main Street
Year: 2026
Region / City: United States
Topic: Economy, Retail, Small Business, Predictions
Document Type: Podcast Transcript
Organization: Main Street America
Author: Matt Wagner
Target Audience: Small business owners, entrepreneurs, retail professionals, nonprofit organizations
Period of Validity: 2026
Approval Date: 2026
Date of Changes: None
Note: Contextual Description
An Assessment of Computational Fluid Dynamics Predictions of the Hydrodynamics of High-Speed Planing Craft in Calm Water and Waves
Year: 2024
Region / City: Nantes, France
Topic: Computational Fluid Dynamics, Hydrodynamics, High-Speed Planing Craft
Document Type: Conference Paper
Organization / Institution: Naval Surface Warfare Center, Naval Hydrodynamics Department, Leidos Corporation, University of Iowa, United States Naval Academy
Authors: T. C. Fu, K. A. Brucker, S. M. Mousaviraad, C. M. Ikeda, E. J. Lee, T. T. O’Shea, Z. Wang, F. Stern, C. Q. Judge
Target Audience: Researchers, Engineers, Hydrodynamics Experts
Period of Application: 2024
Approval Date: 2024
Date of Changes: Not specified
Topics: SHAP Visualizations Predictions