Big Health Data: Cardiovascular Disease Prevention Using Big Data and Machine Learning

Lbrini, Salma; Fadil, Abdelhamid; Aamir, Zakaria; Khomali, Mohamed; Oulidi, Hassane Jarar; Rhinane, Hassan

doi:10.1007/978-3-030-72065-0_17

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Lbrini et al used ML to predict the cardiovascular risk present in the patient to alert them to doctors at an early stage. If a danger emerges, the doctor will get notified, and they will offer diagnosis and guidance to the patients to avoid the health consequences [19]. This study will help the patients to get the treatment as soon as possible, and also it may save lives.…”

Section: Related Researchmentioning

confidence: 99%

OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning

Satra¹,

Sadhu²,

Yanambaka³

et al. 2023

Preprint

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The health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) technique is used to retrieve the health data back and for reducing security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.

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Section: Related Researchmentioning

confidence: 99%

OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning

Satra¹,

Sadhu²,

Yanambaka³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Of the 17 million premature deaths (under 70 years of age) due to non-communicable diseases in 2019, 38% were caused by CVD [14,15]. It is estimated that by 2030, nearly 23.6 million people will die from CVD [16,17]. CVDs include myocardial infarction, atrial fibrillation, and heart failure [18,19].…”

Section: Introductionmentioning

confidence: 99%

Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

et al. 2022

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The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability.

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Big Health Data: Cardiovascular Disease Prevention Using Big Data and Machine Learning

Cited by 2 publications

References 14 publications

OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning

OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning

Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

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