Ata uhi

Recent Trends in Machine Learning for Healthcare Big Data Applications: Review of Velocity and Volume Challenges

I tiakina i:
I whakaputaina i:Algorithms vol. 18, no. 12 (2025), p. 772-806
Kaituhi matua: Khudhur, Doaa Yaseen
Ētahi atu kaituhi: Shibghatullah Abdul Samad, Shaker Khalid, Abdul Latif Aliza, Muda, Zakaria Che
I whakaputaina:
MDPI AG
Ngā marau:
Parallel processing
Accuracy
Data processing
Predictive analytics
Datasets
Big Data
Hardware
Optimization
Feature selection
Data analysis
Machine learning
Health care industry
Medical imaging
Field programmable gate arrays
Innovations
Distributed processing
Efficiency
Velocity
Electronic health records
Health care
Costs
Clustering
Clinical decision making
Algorithms
Real time
Federated learning
Urunga tuihono:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Ngā Tūtohu: Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
Whakarāpopotonga:The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively.
ISSN:1999-4893
DOI:10.3390/a18120772
Puna:Engineering Database