The Life Style Patterns and the Development Trend of Chronic Diseases in Healthy and Sub-healthy Groups Were Analyzed by Using Data-mining Techniques

• Conditions: Chronic Kidney Disease Stage 4; Chronic Kidney Disease Stage 1; Chronic Kidney Disease Stage 2; Metabolic Syndrome; Chronic Kidney Disease Stage 3; Chronic Kidney Disease Stage 5; Chronic Disease

• Registration Number: NCT05225454
• Lead Sponsor: Far Eastern Memorial Hospital

Brief 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.

Detailed 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.

Study Timeline

• Study Start: 2021-03-03
• Study First Submitted: 2021-10-21
• Study First Submitted QC: 2022-01-26
• Study First Posted: 2022-02-04
• Status Verified: 2022-03
• Last Update Submitted: 2022-05-09
• Last Update Posted: 2022-05-10
• Primary Completion: 2024-07-03
• Study Completion: 2024-07-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 81108

Inclusion Criteria

• Continuously health screening twice or more in MJ health reports.
• Chronic kidney disease
• Metabolic syndrome
• Or more, common chronic diseases

Exclusion Criteria

• Participants who have received clinical treatment
• Subjects of other related research diseases

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Number of participants with metabolic syndrome in kidney disease-related adverse events as assessed by estimated glomerular filtration rate	2 year	Physiological information of participants with chronic kidney disease related adverse events as assessed in metabolic syndrome, by natural longitudinal change from baseline in estimated glomerular filtration rate at 2 years recent in health screening in participants.

Trial Locations

Locations (1)

Oriental Institute of Technology / Far Eastern Memorial Hospital; 🇨🇳 New Taipei City, Pan-Chiao Dist., Taiwan