An interpretable Deep Learning Framework for Reliable Diagnosis of Cardiovascular Disease in the Internet of Healthcare Things (IoHT)
Creators: name: Tolba, Ahmed
name: Elmasry, Ahmed
name: Amany Salah
name: Fahmy, Ahmed
name: Attia, Mahmoud
name: Mohamed, Yara
Publisher: name: Zenodo
Year: 2022
Subjects: Deep Learning
Smart Healthcare
Internet of Healthcare Things(IoHT)
Explainable AI
Arrhythmia
electrocardiogram (ECG)

description: <pre>The World Health Organization (WHO) reported that cardiovascular diseases account for 40% of total deaths in Egypt; cardiovascular diseases are a major public health concern with significant social and economic implications in terms of healthcare -needs, lost productivity, and premature death. The disease burden caused by cardiovascular diseases is further fueled by the adoption of unhealthy lifestyles and eating habits. In fact, the incidence of cardiovascular diseases is fast shifting to the youth; a trend that’s specifically prevalent in the capital city of Cairo and underserved urban communities where fast food and sedentary lifestyles are an increasing reality. This in turn makes cardiovascular diseases a great threat to health infrastructure locally and internationally. According to Egyptian vision 2030, healthcare is one of the main pillars to achieve sustainable development and to improve Egyptians' life quality. Therefore, cardiovascular diseases can prohibit the sustainable development process in the Egyptian healthcare system. In recent years, artificial intelligence (AI), especially deep learning (DL), has demonstrated an exciting potential to detect abnormalities in the heart from particular biomarkers. For example, in clinical medicine, the electrocardiogram (ECG) is a critical tool for diagnosing a wide range of arrhythmias. Every year, more than 300 million ECGs are collected around the world, making them a vital tool in the everyday practice of clinical medicine. The existing DL solutions for the diagnosis of cardiovascular diseases from such biomarker data suffer from the black-box nature that limits its applicability for high-risk decisions in real-world healthcare. This in turn makes the doctors unable to trust such a solution. The Internet of Health Things (IoHT) provides a great shift toward automated collection and diagnosis of patients’ data making computer-aided diagnosis (CAD) smarter and more efficient. Coping with Egypt Vision 2030, this project is to take the advantage of advanced AI and IoHT technologies to address the challenges of meeting the diagnosis of cardiovascular diseases threatening the lives of cardiac patients in Egypt. The proposed research project aims to take the advantage of recent advances in DL and IoHT to develop a smart CAD for cardiovascular diseases in IoHT. In the following, we explain the details of the research plan of this project through different stages. first, deep investigation and analysis are performed for electrocardiography and echocardiogram data and its associated features, then, we provide an analytical review and comparatively analyze the previously designed DL models for efficient diagnosis. Second, a solution to the black-box nature of DL approaches is given by introducing an interpretable DL detector that can provide a visual explanation of its detection decisions, which can help doctors to depend on them to make a decision. To ensure perfect interpretations, a human-in-the-loop strategy is followed to involve medical experts to assess and evaluate the quality of interpretations according to the medical properties of the cardiovascular data. Moving away from centralized learning, the project's third stage emphasizes designing a federated approach for preserving the privacy of interpretable DL during the training by applying lightweight key encryption and decryption methods to avoid leaking private patient information. finally, we shift from simulation to physical design by building a small-scale prototype for the IoHT system using edge devices, wireless networks, routers, cloud servers, and fog nodes. Then, we deploy the proposed interpretable DL framework (along with the privacy preservation methods) on this system and their performance is evaluated using the real world samples. </pre>
description: This work is funded by ITAC GP Support Program Round #17 - (ID : GP2022.R17.9)