Integration of Internet of Things and Cloud Computing for Cardiac Health Recognition

Houssein, Essam H.; Ibrahim, Ibrahim E.; Hassaballah, M.; Wazery, Yaser M.

doi:10.1007/978-3-030-70542-8_26

Cited by 5 publications

(3 citation statements)

References 74 publications

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“…The critical advantage of federated learning is that it allows researchers and AI developers to tap into a potentially vast pool of real-world data [15]. Instead of relying simply on the limited amount of publicly available datasets, this method [16] allows resources to be directed toward satisfying clinical objectives and addressing related technical challenges. Nevertheless, further study is required to determine the best algorithmic approaches for federated training, such as how to efficiently combine models or updates and make the system resilient to changes in the population.…”

Section: Related Workmentioning

confidence: 99%

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

Bebortta,

Tripathy,

Basheer

et al. 2023

Diagnostics

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In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal–dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant’s private medical information.

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Section: Related Workmentioning

confidence: 99%

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

Bebortta,

Tripathy,

Basheer

et al. 2023

Diagnostics

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“…As medical technology advances, the usage of crucial medical data for monitoring, prevention, and diagnosis of infectious tumors upsurges. This process usually depends upon managing, collecting, and analyzing the data set for infectious tumor diagnosis and treatment [2,3,4]. The timely detection of a brain tumor can have a positive impact on the patient's chances of survival and quality of life.…”

Section: Introductionmentioning

confidence: 99%

Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning

Syed,

Khan,

Ahmed

et al. 2023

IJEMD-CSAI

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An intracranial tumor is a malignant Central Nervous System (CNS) cancer that significantly contributes to global mortality. Timely prediction of these brain tumors can improve the survival rates of a patient. Magnetic Resonance Imagining (MRI) and Computed Tomography (CT) have emerged as effective non-invasive ways to extract 2D or 3D images of human internal organs, eliminating any pain or surgical procedures. However, analyzing and distinguishing the normal and abnormal tissue is a challenging task. Due to this implying Datadriven approaches, could be a pragmatic way to efficiently classify and detect regions of malignancy of a tumor. The scope of this study is to efficiently predict Brain tumors and their location by employing sophisticated optimized Deep learning models like Convolutional neural Networks (CNN) and Long-Short Term Memory (LSTM) through limited MRI images dataset of human brain. The dataset acquired from kaggle is comprised of 253 MRI images covering different angles of the human brain. The images were of different sizes and shapes, to standardize them data preprocessing simulations were made including resizing, normalizing, etc. For better categorization of model, the images were also converted to binary format (0, 1) by the One Hot Encoding technique. The ratio of training and testing data was taken as 90:10. For CNN and LSTM model, suitable hyperparameters were selected through the trial-and-error technique to ensure the best optimization of the model on the implied training data. The loss and accuracy graph depicts optimized validation and accuracy losses indicating the model to call back early stopping to save computational cost. The predicted labels for both the models and actual label were compared by a confusion matrix which showed accuracy, specificity, recall, and misclassification error to be 91.51, 92.85, 90.12, and 8.49 on average for CNN,while 95.54, 92.86, 94.2, and 5.805 for LSTM model, respectively.

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“…treatment (Akram et al, 2021;Houssein et al, 2021a;Zhang et al, 2021;Javed et al, 2022;Rizwan et al, 2022b). Cancer is one of the most significant health issues and challenges currently threatening people's lives.…”

mentioning

confidence: 99%

Ensemble deep learning for brain tumor detection

Alsubai

Khan

Alqahtani

et al. 2022

Front. Comput. Neurosci.

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With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

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Integration of Internet of Things and Cloud Computing for Cardiac Health Recognition

Cited by 5 publications

References 74 publications

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning

Ensemble deep learning for brain tumor detection

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