Abstract

This research paper presents a bibliometric analysis of the application of soft computing in healthcare, exploring the integration and evolution of these methodologies from 2010 to 2023. Soft computing, encompassing fuzzy logic, neural networks, evolutionary computation, and machine learning algorithms, has significantly influenced healthcare, particularly in diagnostic accuracy, therapeutic efficacy, and patient data analysis. The primary objective of this study is to map the growth patterns, focus areas, and collaborative networks within this field, answering key research questions about the dominant methodologies, evolution over time, geographic and institutional patterns, and future trends in soft computing applications in healthcare. The methodology includes the extraction and analysis of 2932 documents using VOSviewer, focusing on research publication trends, citation analysis, and keyword co-occurrence networks. The main outcomes reveal that journal articles, constituting 97.98% of the documents, are the primary medium for disseminating research in this field. The results demonstrate a growing scholarly interest and significant contributions in soft computing within healthcare, with the United States, China, and India being the most productive countries. Key areas of application include disease diagnosis, medical image analysis, personalized medicine, and healthcare management.