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Binary genetic algorithm for optimal joinpoint detection: Application to cancer trend analysis.

Seongyoon Kim¹, Sanghee Lee², Jung-Il Choi¹

¹School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Korea.

Statistics in Medicine

|November 18, 2020

Summary

This summary is machine-generated.

A new genetic algorithm-based joinpoint (GAJP) model efficiently detects multiple trend changepoints in data. This method improves computational speed over traditional grid search, making it suitable for large cancer incidence datasets.

Keywords:

SEER binary genetic algorithm cancer incidence joinpoint regression

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Area of Science:

Biostatistics
Computational Statistics
Epidemiology

Background:

Joinpoint regression models (JRM) identify trend changes using joinpoints (changepoints).
Existing grid search (GS)-based methods for joinpoint detection are computationally intensive, limiting the number of detectable joinpoints.
Accurate detection of multiple joinpoints is crucial for analyzing complex trends.

Purpose of the Study:

To develop a computationally efficient genetic algorithm-based joinpoint (GAJP) model for optimal joinpoint detection.
To overcome the limitations of existing grid search methods in terms of computational demand and the number of detectable joinpoints.
To evaluate the accuracy and performance of the GAJP model compared to GS-based methods.

Main Methods:

Developed a GAJP model embedding a binary genetic algorithm into the JRM.
Represented joinpoint combinations as binary chromosomes and used genetic operations to minimize fitness functions (Bayesian information criterion - BIC, BIC3).
Evaluated model performance through intensive simulations and comparison with GS-based methods using BIC, BIC3, and permutation tests.

Main Results:

The GAJP model demonstrated outstanding computational efficiency in detecting multiple joinpoints.
The proposed method is practically feasible for detecting multiple joinpoints without preassigning their number.
The GAJP model showed suitability for analyzing large cancer incidence datasets, as demonstrated with colorectal cancer data.

Conclusions:

The GAJP model offers a computationally efficient and feasible approach for detecting multiple joinpoints.
This method is easily extendable to analyze trends in large datasets, such as cancer incidence data.
The GAJP model provides a valuable tool for epidemiological trend analysis and public health research.