HeartCare+: A Smart Heart Care Mobile Application for Framingham-Based Early Risk Prediction of Hard Coronary Heart Diseases in Middle East

Elsayed, Hoda Ahmed Galal; Galal, Mariam Ahmed; Syed, Liyakathunisa

doi:10.1155/2017/9369532

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…The HeartCare+ mobile application, developed by Elsayed et al [13], helps assess the risk of coronary heart disease over 10 years using clinical and nonclinical data, categorizing patients' risk as low, moderate, or high. In addition, Heart-Care+ provides alerts for additional treatment suggestions.…”

Section: B Cvd Prediction Using Machine Learningmentioning

confidence: 99%

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Shajimon,

Shukla,

Patra

2024

Preprint

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Cardiovascular disease (CVD) poses a serious threat to individual health, highlighting the importance of early detection and proactive mitigation. With advancements in consumer electronics such as wearables and IoT, there exists an opportunity for enhanced CVD prediction for users. Machine Learning (ML) has been widely used to predict CVD risk (high/low) based on various factors and is a critical area of healthcare research. However, sharing data needed to predict CVD with machine learning models is challenging due to privacy concerns. Federated Learning (FL) enables distributed training of ML models without sharing raw data. However, it requires all training features to be available to all clients. To address this problem, we propose a Vertical Federated Learning (VFL) based method designed for use with consumer electronics platforms. The proposed method trains Neural Network (NN) model in a distributed manner where different data features are held by different parties. In this work, each party maintains a portion of separate data features, performs calculations on them locally, and then transfers only the necessary information to jointly train an NN model. We employ the proposed method for different use cases where the dataset features are distributed between: i) the patient and the hospital (2-splits); ii) the patient, the doctor, and the laboratory (3-splits); and iii) the patient, the doctor, the Electrocardiogram (ECG) center, and the laboratory (4-splits). Using a realistic dataset publicly available, we test the proposed methodology.

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Section: B Cvd Prediction Using Machine Learningmentioning

confidence: 99%

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Shajimon,

Shukla,

Patra

2024

Preprint

View full text Add to dashboard Cite

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Section: B Cvd Prediction Using Machine Learningmentioning

confidence: 99%

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Shajimon,

Shukla,

Patra

2023

Preprint

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<p>Cardiovascular disease (CVD) encompasses a wide range of diseases that affect the heart and blood vessels, including coronary artery disease, heart failure, arrhythmia, and stroke. Machine Learning (ML) has been widely used to predict CVD risk based on various factors and is a critical area of healthcare research. However, due to privacy concerns, sharing the data needed to predict CVD with ML is challenging. Even though Federated Learning (FL) enables distributed training of ML models without sharing raw data, it assumes that all training features are available to all clients. To address the problem, we propose a Vertical Federated Learning (VFL) based method that trains ML models in a distributed manner and has different features available to several spatial locations. In this work, each party maintains a portion of separate data features, performs calculations on them locally, and then transfers only the necessary information to jointly train an ML model. We employ the proposed method for different use cases where the data characteristics are split between: i) the patient and the hospital (2 splits); ii) the patient, the doctor, and the laboratory (3 splits); and iii) the patient, the doctor, the Electrocardiogram (ECG) center, and the laboratory (4 splits). We test the proposed methodology on the realistic publicly available dataset</p>

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“…Moreover, machine learning can assist doctors in examining patients using digitalized diagnostic information, such as detecting lung, breast, skin cancer, or any other type of cancer disease [6,7]. It has also helped in assessing the risk of sudden cardiac death and other heart diseases based on electrocardiograms and cardiac MRI images [8,9], and medical image classification [10,11]. AI is further proven to have found indicators of diabetic retinopathy in eye images [12].…”

Section: -Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

2022

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Recently, artificial intelligence (AI) has been one of the hottest topics in the technological world. Although it is involved in many domains, it was recently involved in the healthcare sector. AI can be used for diagnostics, drug development, treatment personalization, gene editing, disease prediction, and many more. It helps to improve healthcare services by benefiting medical professionals, hospitals, and patients. Saudi Arabia has a particular interest in the healthcare sector, and it has a clear vision for the future, which points toward the development of AI-based technologies. Few studies investigated the use of AI in Saudi healthcare, and most of them focused on healthcare employees' perceptions. This study is beyond the focus of the existing works. It aims at: 1) presenting the main AI-based healthcare applications; 2) exploring the use of AI in the Saudi healthcare sector; 3) addressing their ethical and legal challenges, along with the policy questions in Saudi healthcare; 4) studying the benefits of these AI-based applications and the acceptance of professionals to use AI in daily practice; 5) introducing the new Personal Data Protection Law (PDPL) in Saudi Arabia; and 6) discussing the importance of AI to the future of Saudi healthcare. To this purpose, a survey was distributed among four main Saudi hospitals. The findings showed that AI should not only lead to better health but also save manpower and simplify the healthcare processes. The respondents agreed that AI helps reflect human intellectual competencies and pushes its limits. Doi: 10.28991/ESJ-2022-06-04-05 Full Text: PDF

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HeartCare+: A Smart Heart Care Mobile Application for Framingham-Based Early Risk Prediction of Hard Coronary Heart Diseases in Middle East

Cited by 9 publications

References 22 publications

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Precision at Heart: An IoT-based Vertical Federated Learning Approach for Heterogeneous Data-Driven Cardiovascular Disease Risk Prediction

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

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