Default and optimized parameters for each predictive model. by Emily S. Nichols (8740029)

Nutrient predictors of the model. by Jari Turkia (17912475)

“…These findings highlight the potential for personalized dietary modifications to optimize nutritional status, enhance patient outcomes, and mitigate the risk of malnutrition in the ESRD population.…”

Table 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.docx by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 5_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 7_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 6_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 3_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 1_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 2_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 4_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Personal details that are used as predictors. by Jari Turkia (17912475)

“…These findings highlight the potential for personalized dietary modifications to optimize nutritional status, enhance patient outcomes, and mitigate the risk of malnutrition in the ESRD population.…”

Table 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Image 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Supplementary Material for: Development of an explainable machine learning model for cardiovascular-kidney-metabolic syndrome prediction based on dietary antioxidants in a national... by figshare admin karger (2628495)

“…Explainable AI approaches such as SHAP enhance model transparency and clinical translation, supporting personalized CKM risk stratification based on dietary antioxidant patterns.…”

The relevant code used in this study. by Jiaxin Jiang (10656134)

“…Using CRC cases from four distinct cohorts, we built and validated a predictive model based on SARS-CoV-2 producing fructose metabolic anomalies by coupling Cox univariate regression and lasso regression feature selection algorithms to identify hallmark genes in colorectal cancer. …”

Image_1_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.JPEG by Payam Hosseinzadeh Kasani (13280397)

“…</p>Results<p>The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. …”

Image_2_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.JPEG by Payam Hosseinzadeh Kasani (13280397)

“…</p>Results<p>The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. …”

Data_Sheet_1_Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.docx by Payam Hosseinzadeh Kasani (13280397)

“…</p>Results<p>The best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. …”