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The applications of machine learning techniques in medical data processing based on distributed computing and the
Sarina Aminizadeh1, Arash Heidari2, Shiva Toumaj3
1Medical Faculty of Islamic Azad University of Tabriz, Tabriz, Iran.
This study reviews distributed computing platforms for medical data processing, finding the Internet of Things (IoT) most common. It highlights challenges and recommends further research for advanced Deep Learning (DL) architectures in healthcare.
Area of Science:
- Medical data processing and healthcare informatics.
- Distributed computing and machine learning applications.
- Information technology in healthcare.
Background:
- Modern healthcare relies on advanced information technologies like the Internet of Things (IoT) and sensor technologies for patient data management.
- Innovations such as distributed computing, Machine Learning (ML), and blockchain are crucial for collecting and processing medical data for informed decision-making.
- The COVID-19 pandemic underscores the need for rapid disease diagnosis, where ML aids radiologists but requires substantial, unified training data.
Purpose of the Study:
- To provide a comprehensive analysis of recent studies on distributed computing platforms in medical data processing.
- To categorize and evaluate platforms including cloud computing, edge, fog, IoT, and hybrid systems.
- To identify trends, challenges, and future directions in applying these technologies to healthcare.
Main Methods:
- Systematic review and analysis of 27 research articles on distributed computing platforms for medical data.
- Evaluation of deployed methods, applications, advantages, drawbacks, datasets, security mechanisms, and Transfer Learning (TL) usage.
- Categorization of platforms into cloud computing, edge, fog, IoT, and hybrid.
Main Results:
- The Internet of Things (IoT) platform was the most utilized environment (43%) for proposed architectures in recent studies.
- The majority of reviewed studies (46%) were published in 2021, indicating a recent surge in research.
- Convolutional Neural Networks (CNNs) were the most popular Deep Learning (DL) algorithm, used in 19.4% of studies.
Conclusions:
- Distributed computing platforms, particularly IoT, are increasingly vital for medical data processing and analysis.
- Deep Learning (DL) development faces challenges in data collection, quality, privacy, and ethics.
- Further research is recommended to develop advanced DL architectures and robust healthcare data analysis models.
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