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Updated: Dec 17, 2025
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Hemogram-based decision tree for predicting the metabolic syndrome and cardiovascular diseases in the elderly
Insights
A decision tree using hemogram data can predict future metabolic syndrome (MetS), hypertension, type 2 diabetes, and cardiovascular diseases (CVD) in elderly individuals. This tool aids in early risk identification for prompt management.
Area of Science:
- Gerontology
- Preventive Medicine
- Biostatistics
Background:
- Metabolic syndrome (MetS) is a growing concern in elderly populations.
- Early prediction of associated risks like hypertension, type 2 diabetes, and cardiovascular diseases (CVD) is crucial.
Purpose of the Study:
- To develop a decision tree model utilizing hemogram parameters for MetS prediction.
- To evaluate the association between baseline MetS probability and future risks of hypertension, type 2 diabetes, and CVD in older adults.
Main Methods:
- A cohort of 40,395 elderly participants (≥60 years) in Taiwan was analyzed.
- A decision tree classification model was built using age, sex, and hemogram data (white blood cell count, hemoglobin, platelet count).
- Participants without MetS at baseline were followed to assess future disease development.
Main Results:
- The decision tree demonstrated modest accuracy (AUC ≈ 0.65) with good generalizability.
- Individuals in the highest tertile of predicted MetS probability showed significantly increased risks for future MetS, type 2 diabetes, hypertension, and CVD.
- Hazard ratios for the highest tertile ranged from 1.14 for hypertension to 1.46 for type 2 diabetes compared to the lowest tertile.
Conclusions:
- A hemogram-based decision tree is effective for identifying elderly individuals at high risk of future metabolic syndrome, hypertension, type 2 diabetes, and CVD.
- This predictive tool can facilitate early intervention and management strategies in geriatric populations.
- The study highlights the utility of routine blood tests in predicting long-term health outcomes.
Background:
This study aimed to build a hemogram-based decision tree to evaluate the association between current probability of metabolic syndrome (MetS) and prediction of future hypertension, type 2 diabetes and cardiovascular diseases (CVD) risk.
Methods:
A total of 40 395 elder participants (≥60 years) were enrolled in a standard health examination program in Taiwan from January 1999 to December 2014. A decision tree classification of the presence or absence of MetS at baseline, using age, sex and hemogram (white blood cell, hemoglobin and platelet) as independent variables, was conducted for the randomly assigned training (70%) and validation (30%) groups. Participants without MetS at baseline (n = 25 643) were followed up to observe whether they developed MetS, hypertension, type 2 diabetes or CVD in the future.
Results:
Modest accuracy of the decision tree in the training and validation groups with area under the curves of 0.653 and 0.652, respectively, indicated an acceptable generalizability of results. The predicted probability of baseline MetS was obtained from decision tree analysis. Participants without MetS at baseline were categorized into three equally sized groups according to the predicted probability. Participants in the third tertile had significantly higher risks of future MetS (hazard ratio 1.40, 95% confidence interval 1.25-1.58); type 2 diabetes (1.46, 1.17-1.83); hypertension (1.14, 1.01-1.28); and CVD (1.21, 1.01-1.44), compared with those in the first tertile.
Conclusions:
Execution of hemogram-based decision tree analysis can assist in early identification and prompt management of elderly patients at a high risk of future hypertension, type 2 diabetes and CVD.
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