Research Directions and Challenges in Bio-Inspired Algorithms for Machine Learning and Deep Learning Models in Healthcare

Abstract

In the recent past, there has been growing interest in bio-inspired algorithms for their potential to enhance machine learning and deep learning models, especially for applications in healthcare. This chapter covers the nascent domain of bio-inspired algorithms applied in healthcare, discussing research directions and challenges. This chapter discusses several bio-inspired techniques: genetic algorithms, artificial neural networks, evolutionary strategies, swarm intelligence, and ant colony optimization - underpinning their flexibility and efficiency in optimizing complex healthcare systems. The chapter also describes how these algorithms have been combined in machine learning and deep learning frameworks that exhibit the ability for feature selection challenges, parameter optimization, and model explainability on healthcare datasets. Moreover, the chapter looks into the state-of-the-art application of bio-inspired algorithms in healthcare, including disease diagnosis, medical image analysis, drug discovery, and recommendation systems for personalized treatment. While there have been promising developments, several challenges persist, involving algorithm scalability, computational complexity, robustness to noise and uncertainty, ethical consideration, and regulatory compliance. The chapter suggests potential research directions that could overcome those challenges, emphasizing an interdisciplinary approach among computer scientists, healthcare professionals, and domain experts.

Ähnliche Arbeiten