Bio-Inspired Algorithms-Based Machine Learning and Deep Learning Models in Healthcare 6.0

Abstract

Recent advancements in deep learning (DL) and machine learning (ML) have opened doors for revolutionary applications in healthcare, with better patient care, diagnosis, and treatment. Bio-inspired algorithms, drawing inspiration from natural processes, have gained attention for their potential to enhance ML and DL models in this field. This paper explores current research directions and challenges in utilizing bio-inspired algorithms for advancing healthcare ML and DL models. We investigate their applications and facilitating feature selection, while acknowledging limitations such as scalability, interpretability, and robustness to noisy healthcare data. Ethical considerations surrounding their use in sensitive healthcare contexts are discussed. Through interdisciplinary collaboration and innovative algorithmic approaches, we strive to overcome these challenges and fully unlock the potential of bio-inspired algorithms in healthcare, ultimately aiming to revolutionize healthcare delivery for improved patient outcomes, personalized treatment strategies, and more accurate diagnoses.

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