Chao‐Yu Guo
Biographic Data
| ID | 7739015 |
|---|---|
| NAME | Chao‐Yu Guo |
| GIVEN NAMES | Chao‐Yu |
| FAMILY NAME | Guo |
| SIGNATURE | GUO C Y |
| AFFILIATIONS | National Yang Ming Chiao Tung University |
| ORCID | 0000-0002-9066-9933 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Understanding Teachers' Interdisciplinary Role Perceptions
An interdisciplinary curriculum provides integration of students' subjects and lives, guiding students to understand and engage with changes in the world through real situations and problems, with teachers playing a critical role. However, there is no assessment tool to explore teachers' interdisciplinary role perceptions (TIRP). In this study, an interdisciplinary role perceptions scale for teachers (TIRPS) was developed through literature analy…
A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine
Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not prov…
The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study,…
The Comprehensive Machine Learning Analytics for Heart Failure
Background : Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart f…
Comparison between American Institute of Medicine Guidelines and Local Recommendation for Gestational Weight Gain in Taiwanese Primiparous Women
No prominent works on this page.
The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study,…
The Comprehensive Machine Learning Analytics for Heart Failure
Background : Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart f…
Comparison between American Institute of Medicine Guidelines and Local Recommendation for Gestational Weight Gain in Taiwanese Primiparous Women
A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine
Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not prov…
Understanding Teachers' Interdisciplinary Role Perceptions
An interdisciplinary curriculum provides integration of students' subjects and lives, guiding students to understand and engage with changes in the world through real situations and problems, with teachers playing a critical role. However, there is no assessment tool to explore teachers' interdisciplinary role perceptions (TIRP). In this study, an interdisciplinary role perceptions scale for teachers (TIRPS) was developed through literature analy…
Artificial Intelligence (3 works) · Computer Science (3 works) · Machine learning (3 works) · Internal Medicine (2 works) · Mathematics (2 works) · Medicine (2 works) · Statistics (2 works) · Analytics (1 work) · Applied Psychology (1 work) · Artificial Intelligence in Healthcare (1 work)