Grey relation between main meteorological factors and mortality

PurposeThis paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.Design/methodology/approachFirst, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.FindingsThe proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.Research limitations/implicationsThe performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.Originality/valueIn designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

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A Study on the Application of Grey Correlation Analysis in Reconstructing the Relationship Between Journalism and Organisational Culture Under the Public Management Model

Zhang

Jia

2023

J. Info. Know. Mgmt.

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With the increasing globalisation of the economy, the global economy has been in recession in recent years. Traditionally, corporate competition stems from assets, material, and entrepreneurial spirit. However, with the times’ development, enterprises’ competitiveness has gradually transformed into cultural competitiveness. Organisational cultural competitiveness combines culture with tangible assets to create a human identity unique to the organisation, thus further enhancing its competitiveness. In order to improve competitiveness, this study reconstructs the organisation’s culture by integrating press and publicity into it, uses grey relation analysis (GRA) to analyse it, and establishes a GRA evaluation model to evaluate competitiveness indicators. The experimental results show that among the three indicators of physical, institutional and spiritual culture competitiveness, the curves of institutional culture competitiveness and organisational competitiveness are the most similar, with a correlation degree of 0.9, indicating that the improvement of institutional culture competitiveness has a greatest impact on the improvement of organisational culture competitiveness. This study proposes an efficient method to improve organisational competitiveness.

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Grey relation between main meteorological factors and mortality

Cited by 5 publications

References 22 publications

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Hospital readmission prediction based on improved feature selection using grey relational analysis and LASSO

A Study on the Application of Grey Correlation Analysis in Reconstructing the Relationship Between Journalism and Organisational Culture Under the Public Management Model

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