In today's world of technology breakthroughs, incorporating cutting-edge procedures has become essential, especially in healthcare, where an early and precise diagnosis is critical for improving patient outcomes a...
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The rise in digital information sharing poses risks to privacy and personal identity, making data vulnerable to theft and swift modifications during transfer. Safeguarding digital data against attackers is crucial. To...
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A significant portion of people have suffered from a form of Parkinson's disease (PD), widely attributed to be the second most frequently diagnosed form of neurological illness that significantly impairs motor and...
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Given the sharp increase of digital content emerging from social media, it is vital to keep an eye on trends in public opinion and attitude on different subjects, events, or problems. With the primary goal of extracti...
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Deep learning breakthroughs have transformed disease prediction and treatment recommendation systems, yet the confidentiality of sensitive medical data remains a major worry. In this paper, we propose a federated lear...
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ISBN:
(纸本)9798350394474
Deep learning breakthroughs have transformed disease prediction and treatment recommendation systems, yet the confidentiality of sensitive medical data remains a major worry. In this paper, we propose a federated learning(FL) methodology that harnesses the capabilities of deep learning models such as Recurrent Neural Networks (RNN), Long ShortTerm Memory Networks (LSTM), Gated Recurrent Units (GRU), and Bidirectional Encoder Representations from Transformers (BERT) to address this challenge. Our approach provides a framework for multiple medical institutions to collaboratively train a deep learning model without sharing patients' private medical records. Leveraging the Flower module, we establish a federated learning architecture comprising decentralized trainers and a global server responsible for aggregating model weights from all trainers. This setup ensures data privacy while enabling the model to learn from the diverse medical records across institutions. The trained model demonstrates significant accuracy in predicting approximately 40 common diseases from rich natural language medical reports. By integrating natural language processing capabilities, our model not only diagnoses ailments but also recommends tailored remedies. Since most of the diseases in our dataset are common and non-severe, users can access these remedies, bypassing the necessity for immediate medical visits, thus saving time and expenses associated with consultations. The proposed methodology offers a viable solution to establish a framework for collaborative learning in healthcare while safeguarding patients' private data- a crucial aspect in today's data driven medical landscape. Additionally, it offers a way to set up a highly accurate disease prediction and remedies recommendation system, empowering users to take proactive measures for their health. BERT+LSTM model which we developed showed an accuracy of 96.5% with centralized training whereas training the model on the FL architecture
Diabetes mellitus is a chronic metabolic disorder affecting millions worldwide. Early detection is crucial for effective management;however, current diagnostic methods often involve invasive procedures or are costly. ...
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Eye tracking is an important variable and offers benefits in Human-computer interaction (HCI) which is often complex and impractical in practice. Eye tracking implementation usually requires specialized devices, techn...
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Federated Learning (FL) is a decentralized training paradigm where clients collaboratively train a Machine Learning (ML) model without outsourcing their raw data. Each participant locally trains a model utilizing thei...
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DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotide...
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Decentralized Autonomous Organizations (DAOs) have become a transformative force in the ever-evolving landscape of decentralized finance (DeFi), reshaping traditional investment frameworks. This research thoroughly ex...
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