Dataset Optimization for Chronic Disease Prediction with Bio-Inspired Feature Selection

TL;DR

This paper explores the use of bio-inspired optimization algorithms for feature selection to improve the accuracy and interpretability of chronic disease prediction models, demonstrating their effectiveness across various diseases.

Contribution

It introduces a comparative analysis of genetic, particle swarm, and whale optimization algorithms for feature selection in chronic disease prediction, highlighting their potential benefits.

Findings

Bio-inspired algorithms effectively reduce features needed for accurate classification.

Performance varies across different datasets and diseases.

Data pre-processing is crucial for reliable results.

Abstract

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.