Version 1
: Received: 5 January 2024 / Approved: 10 January 2024 / Online: 10 January 2024 (04:20:52 CET)
How to cite:
Tarekegn, A.; Alemu, T. Evolutionary Machine Learning for Intelligent Health Risk Prediction. Preprints2024, 2024010753. https://doi.org/10.20944/preprints202401.0753.v1
Tarekegn, A.; Alemu, T. Evolutionary Machine Learning for Intelligent Health Risk Prediction. Preprints 2024, 2024010753. https://doi.org/10.20944/preprints202401.0753.v1
Tarekegn, A.; Alemu, T. Evolutionary Machine Learning for Intelligent Health Risk Prediction. Preprints2024, 2024010753. https://doi.org/10.20944/preprints202401.0753.v1
APA Style
Tarekegn, A., & Alemu, T. (2024). Evolutionary Machine Learning for Intelligent Health Risk Prediction. Preprints. https://doi.org/10.20944/preprints202401.0753.v1
Chicago/Turabian Style
Tarekegn, A. and Tamir Alemu. 2024 "Evolutionary Machine Learning for Intelligent Health Risk Prediction" Preprints. https://doi.org/10.20944/preprints202401.0753.v1
Abstract
Evolutionary Machine Learning (EML) has demonstrated its effectiveness in modeling various real-world application domains, including the dynamics of diseases and the creation of predictive models across diverse fields. However, the challenge of data imbalance adversely impacts the performance of EML models. While strategies involving misclassification costs or cost-sensitive learning exist to address this issue, their integration into EML has yet to be thoroughly explored. This paper aims to fill this gap by investigating the application of cost-sensitive EML for intelligent health risk prediction. More specifically, this paper delves into the utilization of cost-sensitive genetic programming (CSGP) and cost-sensitive genetic algorithms (CSGA), which are among the most widely used algorithms in the EML category. The study entails training CSGP and CSGA to predict the early onset of three chronic conditions (hypertension, diabetes, and fatty acid-related disorders) through the development of an EML model. Subsequently, the model's performance is evaluated through standard fitness functions such as sensitivity, specificity, and F1-score. From the experimental results, EML models demonstrate promising results in identifying the occurrence of chronic diseases. Further optimization of the cost-sensitive EML model holds the potential for enhanced outcomes in modeling medical problems.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
