Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

Nazir, Muhammad; Khan, Muhammad Amir; Qureshi, Sajida; Al-Rasheed, Amal

doi:10.3390/app13031911

Cited by 34 publications

(17 citation statements)

References 40 publications

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“…A survey by Moshawrab et al [43] thoroughly examined federated learning, providing a theoretical explanation and comparing it to other technologies. Disease prediction also highlights the application of federated learning in diagnosing cancer [92], diabetes [82], and cardiovascular diseases [93].…”

Section: Disease Predictionmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

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Section: Disease Predictionmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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“…Skin cancer detection using an enhanced version of FL is proposed in [28,29]. To evaluate the effectiveness of FL for healthcare service providers, the authors in [30,31] propose the upgraded versions of FL for effective disease diagnosis, while ensuring data privacy and achieving better prediction accuracy. A solution to optimization problems using a distributed algorithm is proposed in [32].…”

Section: Related Workmentioning

confidence: 99%

Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

et al. 2023

Self Cite

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The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings.

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“…The Internet of Things (IoT) has changed healthcare in recent years by letting doctors keep an eye on patients' health all the time through smart tech and monitors. Electronic health records (EHRs) and other types of data that these devices create can be used to make models that can predict heart illnesses [1]. Adding Internet of Things (IoT) devices to healthcare systems could make care better by giving doctors more real-time information about their patients' health.…”

Section: Introductionmentioning

confidence: 99%

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

Pranjal Pandit, Yogesh J. Gaikwad, Amruta Mhatre

2024

jes

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Predicting heart diseases is important for finding them early and treating them effectively. We present a shared learning method for predicting heart diseases using IoT-based electronic health records (EHRs) in this work. Federated learning lets many autonomous IoT devices work together to train a model, while protecting the safety and security of the data. Proposed method uses the fact that IoT devices are spread out to train a global model for predicting heart disease without putting private EHR data in one place. With the EHR data, each IoT device learns a model locally and only sends model changes to a central computer. The computer takes all of these changes and improves the world model. This model is then sent back to the IoT devices to be improved even more. This looping process makes sure that the world model keeps getting better while keeping data private. The proposed method tested by using a real-world collection of EHRs from IoT devices in trials. We looked at how well our shared learning method worked compared to more standard centralized learning methods. Our results show that the pooled learning method makes predictions that are as good as or better than the other methods while protecting data privacy. It also looked at how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. It is discovered that devices with more processing power and more data add more to the improvement of the global model. This shows how important it is to choose the right devices in shared learning systems. The paper study shows that pooled learning can be used to predict heart diseases in IoT-based EHRs and that it works well. Our method uses the ability of IoT devices to work together to make accurate predictions while protecting data privacy. This makes it suitable for use in real-life healthcare situations.

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Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

Cited by 34 publications

References 40 publications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

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