Related Experiment Video
Updated: Aug 1, 2026
Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
Published on: October 31, 2010
A score-based method of immune status evaluation for healthy individuals with complete blood cell counts
Min Zhang1, Chengkui Zhao1, Qi Cheng1
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Insights
A new immunity score, derived from complete blood cell counts (CBC), helps assess the immune status of healthy individuals. This score correlates with age and aids in early disease risk detection.
Area of Science:
- Immunology
- Biostatistics
- Computational Biology
Background:
- Growing concern over immune status during the COVID-19 pandemic.
- Lack of effective methods to assess immune status in healthy individuals.
- Need for early disease risk detection algorithms.
Purpose of the Study:
- To develop a novel algorithm for assessing immune status in healthy individuals.
- To utilize complete blood cell counts (CBC) for immune status evaluation.
- To provide an early warning system for potential health risks.
Main Methods:
- Collected CBC data from 16,715 healthy individuals.
- Developed a three-platform normalization model and used expectation maximization Gaussian mixture model (EM-GMM) for clustering.
- Employed Random Forest, LightGBM, and XGBoost to identify CBC index correlations with immune status.
- Constructed a weighted sum model to generate a continuous immunity score.
Main Results:
- Established a significant negative correlation between the immunity score and age in healthy individuals.
- Developed a nonlinear polynomial regression model to describe the age-immunity score relationship.
- Demonstrated the method's effectiveness in evaluating individual immune status against age-specific references.
Conclusions:
- Successfully developed a novel model for evaluating immune status in healthy populations.
- The proposed method offers a valuable approach for early detection of abnormal immune status.
- This tool is significant for early warning of infectious disease risks.
Background:
With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19.
Methods:
First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status.
Results:
The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual's immune status with the reference value corresponding to their age, their immune status can be evaluated.
Conclusion:
In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.
