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

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. …”

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. …”

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.…”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”

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.…”

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

“…Multiple SVM models were trained and evaluated, including configurations with linear and RBF (Radial Basis Function) kernels. </p><p dir="ltr">Additionally, an exhaustive hyperparameter search was performed using GridSearchCV to optimize the C, gamma, and kernel parameters (testing 'linear,' 'rbf,' 'poly,' and 'sigmoid'), aiming to find the highest-performing configuration. …”

Nutritional strategies in the rehabilitation of musculoskeletal injuries in athletes: a systematic integrative review - PROTOCOL by Diego A. Bonilla (9086201)

“…The overall assessment of the risk of bias for each outcome was presented as: "low risk", "some concerns" or "high risk" of bias. …”

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

“…In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. …”

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. …”