A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data...

Area of Science:

• Computer Science
• Healthcare Technology
• Data Science

Background:

• Machine learning (ML) and deep learning (DL) are increasingly vital in healthcare, especially with big data analytics.
• Applications include predictive analytics, medical image analysis, drug discovery, personalized medicine, and EHR analysis.
• The healthcare sector faces challenges with high-volume, unstructured medical data.

Purpose of the Study:

• To examine research trends in the adoption of machine learning and deep learning in the healthcare sector.
• To provide a comprehensive bibliometric analysis of scientific literature.
• To guide academics, researchers, and healthcare professionals in understanding and directing future research.

Main Methods:

• Utilized Web of Science (WoS) database for SCI/SCI-E/ESCI journals.
• Performed bibliometric statistical analysis (year-wise, nation-wise, affiliation-wise, etc.).
• Employed VOSviewer software for network analysis (authors, sources, countries, institutions, trending terms).

Main Results:

• Analysis of research trends in ML and DL adoption within healthcare.
• Identification of key research areas, influential authors, institutions, and global collaborations.
• Mapping of citation, co-citation, and trending term networks.

Conclusions:

• ML and DL, coupled with big data, hold revolutionary potential for healthcare.
• These technologies can improve patient outcomes, reduce costs, and accelerate new treatment development.
• The study offers insights to direct future research and decision-making in healthcare ML/DL adoption.