A privacy-preserving logistic regression-based diagnosis scheme for digital healthcare

Zhou, Yousheng; Song, Liang; Liu, Yuanni; Vijayakumar, Pandi; Gupta, Brij B.; Alhalabi, Wadee; Alsharif, Hind

doi:10.1016/j.future.2023.02.022

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…LR stands as a highly prevalent classifier employed for binary classification tasks [25]. It aims to discover a function that has the capability of predicting outcomes for a binary dependent variable based on one or multiple independent variables.…”

Section: Logistic Regressionmentioning

confidence: 99%

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

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Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model's performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.

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Section: Logistic Regressionmentioning

confidence: 99%

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Sharma,

Dhanka,

Kumar

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…The patient’s data used in healthcare applications are vulnerable to several attacks that breach the patient’s privacy or compromise the correctness of the diagnosis [ 17 ]. Several protection approaches have been proposed based on cryptographic techniques, such as homomorphic encryption, or based on the patient’s anonymization [ 24 , 25 , 26 ]. Homomorphic encryption is implemented within ML algorithms to perform operations directly on encrypted data to ensure the confidentiality of the data.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition

Castro,

Impedovo,

Pirlo

2023

Sensors

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Human activity recognition (HAR) through gait analysis is a very promising research area for early detection of neurodegenerative diseases because gait abnormalities are typical symptoms of some neurodegenerative diseases, such as early dementia. While working with such biometric data, the performance parameters must be considered along with privacy and security issues. In other words, such biometric data should be processed under specific security and privacy requirements. This work proposes an innovative hybrid protection scheme combining a partially homomorphic encryption scheme and a cancelable biometric technique based on random projection to protect gait features, ensuring patient privacy according to ISO/IEC 24745. The proposed hybrid protection scheme has been implemented along a long short-term memory (LSTM) neural network to realize a secure early dementia diagnosis system. The proposed protection scheme is scalable and implementable with any type of neural network because it is independent of the network’s architecture. The conducted experiments demonstrate that the proposed protection scheme enables a high trade-off between safety and performance. The accuracy degradation is at most 1.20% compared with the early dementia recognition system without the protection scheme. Moreover, security and computational analyses of the proposed scheme have been conducted and reported.

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“…In [22], a road crash zone was modeled using LR to determine the nature of the accidents and the types of people that were involved. In a similar vein, a privacy-protecting LRbased diagnosis method for digital healthcare was presented in [23]. The effectiveness of machine-learning RL for tomography processing was shown in [24].…”

Section: Introductionmentioning

confidence: 99%

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

Alshutayri

2023

Eng. Technol. Appl. Sci. Res.

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This paper highlights how the proliferation of online transactions, especially those involving the use of credit cards, has resulted in the emergence of new security flaws that pose threats to customers and enterprises worldwide. E-commerce and other forms of online monetary transactions have become essential in the manufacturing and service sectors, propelling the global economy. The widespread and dependent connectivity of mobile payment systems using credit card transactions presents chances for fraud, risk, and security breaches. In light of the importance of accurately predicting fraud incidents through payment procedures, this study investigated the credit card payment methods used for movie tickets, using the machine learning logistic regression method to analyze and predict such incidents. This study used a dataset from cinema ticket credit card transactions made in two days of September 2013 by European cardholders, including 284,807 transactions out of which 492 were fraudulent purchases. The results of the proposed method showed a prediction accuracy of 99%, proving its high prediction performance.

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A privacy-preserving logistic regression-based diagnosis scheme for digital healthcare

Cited by 11 publications

References 19 publications

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

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