Data Sheet 1_Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms.csv by Hongjun Tao (21448853)

“…Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. …”

Senescence/Aging-related pathways for GSVA analysis. by Xihao Shen (20347942)

Subjects:

Posterior predicted sensitivity, specificity, PPV, and NPV under Model-3 in subgroup analysis by age (a) and comorbidity (b) groups. by Jia Wei (120974)

Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

13 rejection predictive senescence-related genes(pSRGs). by Xihao Shen (20347942)

Subjects:

Confusion Matrix for the Hybrid algorithms. by Faten Al-hussein (20707521)

“…This study aims to develop hybrid prediction models that integrate the strengths of multiple algorithms to enhance HbA1c prediction accuracy while minimising the number of significant Key Performance Indicators (KPIs). …”

Table 1_Development and validation of machine learning models for MASLD: based on multiple potential screening indicators.docx by Hao Chen (5190)

Age distribution of addicted users. by Jing He (58446)

“…In addition, this study used decision tree algorithm to predict adolescent virtual reality device addiction, with a prediction accuracy of 0.957.…”

Data Sheet 1_Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study.docx by Yuanxi Luo (18285568)

“…Following baseline characteristic comparisons and CVD incidence rate calculations, we implemented multiple Cox regression models to assess CMI’s cardiovascular risk prediction capabilities. …”

Violin plot for numerical feature age. by Mst. Rokeya Khatun (22226685)

“…This study investigates the factors influencing school dropout among students aged 6–24 years, employing data from the 2019 Multiple Indicator Cluster Survey (MICS). …”

Table 1_A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data.docx by Fengchun Ren (21596462)

“…However, the combined effects of multiple metals and the modulatory roles of demographic variables remain insufficiently explored.…”

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…</p>Methods<p>We analyzed ~6,000 candidate predictors from multiple knowledge domains (cognitive, psychosocial, neural, biological) contributed by children of late elementary school age (9–10 yrs) and their parents in the ABCD cohort to construct individual-level models predicting the later (11–12 yrs) onset of depression, anxiety and somatic symptom disorder using deep learning with artificial neural networks. …”

GSVA analysis and Immunological analysis. by Xihao Shen (20347942)

Subjects:

Cell communication and establishment of senescence score. by Xihao Shen (20347942)

Subjects:

Reverse cumulative distribution and boxplot of residual for model performance comparison. by Xihao Shen (20347942)

Subjects:

S1 File - by Xihao Shen (20347942)

Subjects:

Discovering two KTx rejection subtypes. by Xihao Shen (20347942)

Subjects:

Quality control and batch effect removal of scRNA analysis. by Xihao Shen (20347942)

Subjects:

Features of 13 pSRGs obtained by machine learning methods. by Xihao Shen (20347942)

Subjects:

Single-cell analysis of KTx rejection and healthy individuals. by Xihao Shen (20347942)

Subjects: