  In this study, we investigated the application of bio-inspired optimization
algorithms, including Genetic Algorithm, Particle Swarm Optimization, and Whale
Optimization Algorithm, for feature selection in chronic disease prediction.
The primary goal was to enhance the predictive accuracy of models streamline
data dimensionality, and make predictions more interpretable and actionable.
  The research encompassed a comparative analysis of the three bio-inspired
feature selection approaches across diverse chronic diseases, including
diabetes, cancer, kidney, and cardiovascular diseases. Performance metrics such
as accuracy, precision, recall, and f1 score are used to assess the
effectiveness of the algorithms in reducing the number of features needed for
accurate classification.
  The results in general demonstrate that the bio-inspired optimization
algorithms are effective in reducing the number of features required for
accurate classification. However, there have been variations in the performance
of the algorithms on different datasets.
  The study highlights the importance of data pre-processing and cleaning in
ensuring the reliability and effectiveness of the analysis.
  This study contributes to the advancement of predictive analytics in the
realm of chronic diseases. The potential impact of this work extends to early
intervention, precision medicine, and improved patient outcomes, providing new
avenues for the delivery of healthcare services tailored to individual needs.
The findings underscore the potential benefits of using bio-inspired
optimization algorithms for feature selection in chronic disease prediction,
offering valuable insights for improving healthcare outcomes.
