Performance comparison with other papers. حسب Hang Zhao (143592)

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Supplementary file 1_The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia.doc حسب Huayan Zuo (22308277)

"…Objectives<p>This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold partitioning, in predicting sarcopenia based on computed tomography (CT) and magnetic resonance imaging (MRI) data.…"

Solution results of different algorithms. حسب Meilin Zhu (688698)

"…Firstly, from the perspective of data-driven, it crawls the historical data of driving speed through Baidu map big data platform, and uses a BP neural network optimized by genetic algorithm to predict the driving speed of vehicles in different periods. …"

Decision model based on the NSO. حسب Hui Zhao (7395)

الموضوعات:

Prediction performance (BSS) for simulated datasets with simulated missingness: low correlation (0.1) and high AUROC (0.9). حسب Elena Albu (15181070)

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Distribution of original (non-missing) and imputed values for continuous variables. حسب Elena Albu (15181070)

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Prediction performance (BSS) for simulated datasets with simulated missingness: high correlation (0.7) and high AUROC (0.9). حسب Elena Albu (15181070)

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Imputed variable and predictor matrix. حسب Elena Albu (15181070)

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NMSE errors (deviations from true values) on test sets for simulated datasets with simulated missingness: high correlation (0.7) and high AUROC (0.9). حسب Elena Albu (15181070)

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Amputation mechanisms. حسب Elena Albu (15181070)

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Median object size of the imputation object in MB (the Q1 - Q3 range is presented in the shiny app). حسب Elena Albu (15181070)

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Prediction performance for datasets with missing values and categorical outcome (BSS). حسب Elena Albu (15181070)

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NMSE errors (deviations from true values) on test sets for simulated datasets with simulated missingness: low correlation (0.1) and low AUROC (0.75). حسب Elena Albu (15181070)

الموضوعات:

Prediction performance (BSS) for simulated datasets with simulated missingness: low correlation (0.1) and low AUROC (0.75). حسب Elena Albu (15181070)

الموضوعات:

NMSE errors (deviations from true values) on test sets for simulated datasets with simulated missingness: high correlation (0.7) and low AUROC (0.75). حسب Elena Albu (15181070)

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Real datasets description. حسب Elena Albu (15181070)

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Mean number of completely missing observations over all train/test splits for datasets with simulated missingness (amputation). حسب Elena Albu (15181070)

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Details on imputation methods in R or python. حسب Elena Albu (15181070)

الموضوعات:

Prediction performance (BSS) for simulated datasets with simulated missingness: high correlation (0.7) and low AUROC (0.75). حسب Elena Albu (15181070)

الموضوعات:

Proportion of original (non-missing) and imputed values for categorical variables. حسب Elena Albu (15181070)

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