A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model

Munagala, N V L M Krishna; Langoju, Lakshmi Rajeswara Rao; Rani, Asha; Reddy, D. V. Rama Koti

doi:10.1016/j.bbe.2022.10.001

Cited by 23 publications

(4 citation statements)

References 45 publications

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“…An improved lesion image extraction method is proposed in [44], which uses a combination of multi-scale morphological local variance reconstruction and fast fuzzy C-means clustering. Improved skin cancer prediction methods are developed by researchers such as an all-inclusive application [45], combination of adaptive region growth and neuromorphic clustering [46], hybrid metaheuristics for enhance image boundaries estimation [47], DL-based technique BF2SkNet for optimal feature [48], deep neural network with features fusion and selection [49], hybrid deep whale optimization with entropy-mutual information (EMI) method [50], enhanced classification technique [51], modified meta-heuristic technique for feature selection [52], a hybrid classification method with feature optimization [53], enhanced cost estimation using adaptive multi-cost function [54,55], and an optimal feature extraction using the Henry Gas Solubility Optimization algorithm [56].…”

Section: Dataset Attributesmentioning

confidence: 99%

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

et al. 2023

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Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis.

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Section: Dataset Attributesmentioning

confidence: 99%

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

et al. 2023

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“…In this section, we conducted a comprehensive comparison of the performance of the designed model in terms of precision, F-measure, accuracy, and recall. These evaluations were made in relation to several recent existing techniques, including, Support Vector Machine with Artificial Neural Network (SVM-ANN) [40], Artificial Neural Networkbased Cardiovascular Disease Prediction (ANNbCDP) [41], Multi-Layer Perceptron for Enhanced Brownian Motion based on Dragonfly Algorithm (MLP-EBMDA) [42], Genetic Algorithm-based Neural Network (GAbNN) [43], Genetic Algorithm with Particle Swarm Optimization (GA-PSO) [44], Multi-Label Active Learning-based Machine Learning (MALbML) [45], Harris Hawk Optimization-based Clustering Algorithm (HHObCA) [46], Bayesian Optimization-based Extreme Gradient Boosting (BObEGB) [47], and Harris Hawk Optimization with Fuzzy Long Short-Term Memory (HHO-FbLSTM) [48]. This thorough comparison allows us to gauge the effectiveness and superiority of the designed model over these existing techniques across multiple performance metrics.…”

Section: Comparison Of Model Performances With Existing Techniquesmentioning

confidence: 99%

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

Alqurashi,

Zafar,

Khan

et al. 2023

Mathematics

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Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors.

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“…Monitoring systems for home care are plentiful but are constrained by several limitations [5]. Therefore, the IoT continuous cardiac monitoring system is one of the applications that is available 24/7 [6]. The integrated system includes a synchronized API and a mobile application for predicting and alerting about the situation [7].…”

Section: Introductionmentioning

confidence: 99%

Remote Heart Rate Monitoring Device Using the Internet of Things

Iparraguirre-Villanueva,

Surcco-Jacinto,

Balvin-Chávez

2024

Int. J. Onl. Eng.

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Cardiovascular diseases are the leading cause of death worldwide. Therefore, this study aims to develop a mobile application utilizing the Internet of Things (IoT) to monitor patients’ heart rate. The study employed a quantitative approach and a pre-experimental design. The experiment was conducted according to the research plan and involved 20 patients. The Scrum methodology was used for the development of the mobile application. The results reveal a significant improvement in patient and family satisfaction after using the IoT-enabled mobile application. In addition, the average measurement time has decreased to 6.025 minutes, which represents a significant difference compared to the traditional method. The number of measurements has increased from seven to 14 per week, averaging two regular daily measurements. The measurement device has alleviated the concerns of family members who are taking care of loved ones with cardiovascular disease. This tool gives users greater peace of mind, enabling them to take accurate and reliable measurements 24/7.

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A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model

Cited by 23 publications

References 45 publications

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning

Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases

Remote Heart Rate Monitoring Device Using the Internet of Things

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