Soft computing techniques for biomedical data analysis: open issues and challenges

Abstract

In recent years, medical data analysis has become paramount in delivering accurate diagnoses for various diseases. The plethora of medical data sources, encompassing disease types, disease-related proteins, ligands for proteins, and molecular drug components, necessitates adopting effective disease analysis and diagnosis methods. Soft computing techniques, including swarm algorithms and machine learning (ML) methods, have emerged as superior approaches. While ML techniques such as classification and clustering have gained prominence, feature selection methods are crucial in extracting optimal features and reducing data dimensions. This review paper presents a comprehensive overview of soft computing techniques for tackling medical data problems through classifying and analyzing medical data. The focus lies mainly on the classification of medical data resources. A detailed examination of various techniques developed for classifying numerous diseases is provided. The review encompasses an in-depth exploration of multiple ML methods designed explicitly for disease detection and classification. Additionally, the review paper offers insights into the underlying biological disease mechanisms and highlights several medical and chemical databases that facilitate research in this field. Furthermore, the review paper outlines emerging trends and identifies the key challenges in biomedical data analysis. It sheds light on this research domain’s exciting possibilities and future directions. The enhanced understanding of soft computing techniques and their practical applications and limitations will contribute to advancing biomedical data analysis and support healthcare professionals in making accurate diagnoses.