A novel blockchain-enabled heart disease prediction mechanism using machine learning

Hasanova, Huru; Tufail, Muhammad; Baek, Ui-Jun; Park, Jee-Tae; Kim, Myung-Sup

doi:10.1016/j.compeleceng.2022.108086

Cited by 48 publications

(21 citation statements)

References 17 publications

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“…The output sequence are independent of each other, and the multi-headed attention mechanism module can focus on different information at different locations in the representation subspace at the same time. The multi-headed attention mechanism is calculated as shown in Eqs ( 2) and (3).…”

Section: Plos Onementioning

confidence: 99%

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Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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In recent years, as the Ether platform has grown by leaps and bounds. Numerous unscrupulous individuals have used illegal transaction to defraud large sums of money, causing billions of dollars of losses to investors worldwide. Facing the endless stream of the illegal transaction based on Ether smart contracts problems, such as illegal transaction, money laundering, financial fraud, phishing. Currently, illegal transaction are only detected by a single view of the smart contract’s contract code view feature and account transaction view feature, which is not only incomplete, but also not fully representative of the smart contract’s features. More importantly, the single view detection model cannot accurately capture the global structure and semantic features between the Tokens of the view features. In this case, it is particularly important that all view features are shared among themselves. In this paper, we investigate a Transformer-based model for contrasting illegal transaction detection networks under multiple views (TranMulti-View Net). The model in this paper is based on Transformer to learn a multi-view fusion representation, which aims to maximise the fusion of the interaction information of different view features under the same condition. In this model we first use the Transformer model to learn global structure and semantic features from a sequence of Tokens tokenised by a view, capturing the remote dependencies of Tokens in the view features, and then we share the contract code view features and the account transaction view features across all views to learn important semantic information between views from each other. In addition, we find that the approach of semi-supervised training of multi-view features using contrast learning outperforms the scheme of prediction based on direct fusion of different view features, resulting in stronger correlation between view features. As a result, the underlying semantic information can be captured more accurately, leading to more accurate predictions of illegal transaction. The experimental results show that our proposed TranMulti-View Net obtains good detection results with a Precision score of 98%.

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Section: Plos Onementioning

confidence: 99%

“…Blockchain, derived from Bitcoin, is a hot topic of research in industry and academia. The blockchain source application scenario has also been extended to healthcare [1][2][3], the Internet of Things [4] and the economy [5], and is therefore also known as the next generation of the Internet.…”

Section: Introductionmentioning

confidence: 99%

Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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“…A work related to integrating both was proposed by Reference 133. It focused on storing patient data and training the ML model to predict heart disease.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence‐based blockchain solutions for intelligent healthcare: A comprehensive review on privacy preserving techniques

Gami,

Agrawal,

Mishra

et al. 2023

Trans Emerging Tel Tech

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While blockchain technology (BT) is considered secure, there are several vulnerabilities that can breach its security. The study in artificial intelligence (AI) and BT is widely popular due to its expanding importance in enhancing security and computational prowess. In this study, we present a comprehensive and meticulous comprehensive review of AI and BT‐based privacy‐preserving smart healthcare. The selection for this study was based on a holistic and integrated approach which involved examining not only individual studies but also their relationships, and trends. Through a systematic analysis of various phases, we identified 91 primary studies pertaining to information needed to stockpile directions called for retorting the research queries. We have undertaken a descriptive comparison of foundational manuscripts, taking into account an array of essential factors, including performance metrics, security protocols, and computational prowess. Our thorough discussions and debates have led to the identification of research gaps in the current manuscript, as well as the direction for future research. We also propose our constructive approach for the aforementioned integration, highlighting its potential benefits and implications.

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“…Where ri is the input data, b1 is the bias, m is total number of input neurons, and j is total number of neurons in the hidden layer. Here is a list of the output layer activation functions by eqn (2).…”

Section: 1mentioning

confidence: 99%

“…Both the sender and the receiver of traditional financial transactions must rely on a Trusted Third Party (TTP), such as a bank. It involves a few operational and security issues [2]. A TTP, for instance, gains access to a user's financial information, indicating a breach of user privacy.…”

Section: Introductionmentioning

confidence: 99%

Blockchain machine learning based personal health record data analysis with smart decentralization and security enhancement

Shah

Rai

Singh

et al. 2023

Preprint

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An essential component of a patient's follow-up is a medical record. It includes opinions, prescriptions, analyses, and all patient data of healthcare professionals. Process of sharing as well as managing this file involves a number of players, including patient, doctor, and pharmacist. Electronic medical record (EMR) can be accessed by any authorized individual from any location, and data are shared among various health service providers. Using blockchain machine learning techniques, this study proposes a novel method for personal health records (PHR) -based data analysis and network security enhancement. Variational Boltzmann spatio encoder neural networks were utilized for the data analysis of personal health records. The decentralized blockchain architecture enhances network security. Based on network security and data analysis, the experimental analysis is conducted in terms of random accuracy 81%, specificity55%, latency 62%, QoS 52%, and computational cost 41%.

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A novel blockchain-enabled heart disease prediction mechanism using machine learning

Cited by 48 publications

References 17 publications

Transformer-based comparative multi-view illegal transaction detection

Transformer-based comparative multi-view illegal transaction detection

Artificial intelligence‐based blockchain solutions for intelligent healthcare: A comprehensive review on privacy preserving techniques

Blockchain machine learning based personal health record data analysis with smart decentralization and security enhancement

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