Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05225454 |
| Other study ID # |
110027-E |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 3, 2021 |
| Est. completion date |
July 31, 2024 |
Study information
| Verified date |
March 2022 |
| Source |
Far Eastern Memorial Hospital |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
Used multi-year health examination member profile by multi-algorithms technology, to find
comprehensive key hazard factors or important high-risk group components for metabolic
syndrome and chronic kidney disease or more common chronic diseases.
Description:
The proportion of the population over the age of 65 in Taiwan reached 7.10% in 1993. After
Taiwan became an 「aging country」, the originally slow growth of the elderly population (9.9%
in 2006) started to increase, and it reached 14.05% in 2018, which was almost 2 times that in
1993. In addition, Taiwan formally became an 「aged country」as defined globally. According to
the statistical data from the Ministry of the Interior and the data from the National
Development Council, it is estimated that the population over the age of 65 is rapidly
growing. It is expected that 6 years later (by 2026), the elderly population in Taiwan will
exceed 20%. Taiwan will formally become the「super-aged country」as defined globally, with a
population structure similar to that in Japan, South Korea, Singapore, and some European
countries (Department of Statistics, 2018; National Development Council, 2019). In order to
effectively prevent and treat chronic diseases of sub-health populations and develop health
management prediction systems that have unlimited market opportunities and potentials, the
author intends to extend the achievements of individual projects sponsored by the Ministry of
Science and Technology in recent years. By multi-year complete health examination member
profile, this project used multiple algorithms, such as Logistic regression (LR);
Classification And Regression Trees (CART); Hierarchical Linear Modeling (HLM); Random
forests (RF); Support-Vector Machines (SVM); eXtreme Gradient Boosting (xGBoost); Light
Gradient Boosting Machine (LightGBM) and multiple analysis tools to explore the common
potential health hazard variables of the sub-health population to establish a comprehensive
assessment health management system that can detect chronic diseases early, the research
results will be provided for reference in related fields.
