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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.

Deren Xu¹, Weng Howe Chan², Habibollah Haron¹

¹Faculty of Computing, Universiti Teknologi Malaysia, Faculty of Computing, Johor, Johor Bahru, Malaysia.

Peerj. Computer Science

|August 15, 2024

Summary

This summary is machine-generated.

Multi-objective optimization effectively selects infectious disease prediction models, improving accuracy and efficiency. Decision tree and XGBoost models showed superior performance over traditional methods for public health applications.

Keywords:

Infectious disease prediction Model selection Multi-objective optimization NSGA-II Public health

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Area of Science:

Public Health
Computational Biology
Optimization Methods

Background:

Global public health challenges necessitate advanced infectious disease prediction models.
Selecting optimal models is crucial for accurate forecasting and resource allocation.

Purpose of the Study:

To investigate the application of multi-objective optimization in selecting infectious disease prediction models.
To evaluate the impact of multi-objective optimization on prediction accuracy, generalizability, and computational efficiency.

Main Methods:

Utilized the NSGA-II algorithm for multi-objective optimization.
Compared models selected via multi-objective optimization against those from single-objective optimization.
Evaluated models including Decision Tree (DT) and Extreme Gradient Boosting Regressor (XGBoost).

Main Results:

Multi-objective optimization selected DT and XGBoost models that outperformed others in accuracy, generalizability, and efficiency.
DT and XGBoost models showed significantly lower Root Mean Square Error (RMSE) compared to ridge regression selected by single-objective methods.
Demonstrated the effectiveness of multi-objective optimization in balancing multiple performance metrics.

Conclusions:

Multi-objective optimization offers significant advantages for selecting infectious disease prediction models.
Findings highlight the theoretical and practical importance of these methods for public health decision support systems.
Future research should explore algorithm enhancements, broader metrics, and diverse datasets.