The Impact of Digital Economy on Economic Growth Based on Pearson Correlation Test Analysis

Chen, Huafu; Gospodarik, C. G.

doi:10.1007/978-981-16-5854-9_3

Cited by 8 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17 In order to analyze the correlation between the obtained indicators {X 18 . Based on the different data types, Pearson correlation analysis 19 is first performed on the pairwise continuous indicators to determine the differences between the various indicators. The Pearson correlation coefficient is the covariance between two variables divided by their…”

Section: Analysis Layermentioning

confidence: 99%

Construction of Non-invasive Pre-diagnostic Index System for Diabetes Based on CACP Multilayer Screening Framework

Chai

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

Diabetes is becoming more and more prevalent, and preventing its occurrence has become a major challenge for society. In response, this article has established a non-invasive pre-diagnostic indicator system for diabetes using the CACP multi-layer data processing framework. The system consists of four main layers: cleaning, analysis, cross-validation, and processing. In the cleaning layer, indicators and subject groups with a high missing rate undergo screening, and missing data is processed through interpolation. The analysis layer analyzes the correlations between indicators of different types and categories, verifies and eliminates highly correlated indicators, and obtains a preliminary screening indicator set. In the cross-validation layer, the primary screening indicator set undergoes further processing to obtain a secondary indicator set. In the processing layer, the normalized and processed data is input into the xgboost classifier to obtain the AUC value and accuracy as a reference. Each indicator in each secondary indicator set is then deleted and processed to select the group of indicators with better results than the control group. Finally, the selected indicators are validated through the processing layer to obtain the best classification performance in the final screening feature set. Using part of the features and classification data from the Nhanes data in 2017-2018, the non-invasive pre-diagnostic indicator system for diabetes can achieve a classification accuracy of 0.91. This system only requires routine physical examination data such as height, weight, blood pressure, and some personal information to achieve autonomous non-invasive pre-diagnosis, making it an effective tool to prevent and treat diabetes.

show abstract

Section: Analysis Layermentioning

confidence: 99%

Construction of Non-invasive Pre-diagnostic Index System for Diabetes Based on CACP Multilayer Screening Framework

Chai

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Employee Development and Digitalization in BANI World

Evseeva

Rawat³

2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Цифровизация Как Фактор Повышения Конкурентоспособности Строительной Отрасли

Господарик

Тайшань

2023

jour

View full text Add to dashboard Cite

Проанализировано состояние мирового рынка строительных услуг и динамика конкурентных позиций мировых лидеров – Китая и ЕС, а также Республики Беларусь по методике [2]. Обоснованы предложения по трансферу китайского опыта цифровизации в строительный комплекс Беларуси.

show abstract

The Impact of Digital Economy on Economic Growth Based on Pearson Correlation Test Analysis

Cited by 8 publications

References 10 publications

Construction of Non-invasive Pre-diagnostic Index System for Diabetes Based on CACP Multilayer Screening Framework

Construction of Non-invasive Pre-diagnostic Index System for Diabetes Based on CACP Multilayer Screening Framework

Employee Development and Digitalization in BANI World

Цифровизация Как Фактор Повышения Конкурентоспособности Строительной Отрасли

Contact Info

Product

Resources

About