Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites, Ivo Sérgio Guimarães; Silva, Lidia Martins da; Barbosa, Jorge Luís Victória; Rigo, Sandro José; Correia, Sérgio D.; Leithardt, Valderi Reis Quietinho

doi:10.20944/preprints202110.0161.v1

Cited by 5 publications

(2 citation statements)

References 80 publications

(199 reference statements)

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“…The literature review involved an in-depth exploration of the existing research and knowledge pertaining to heart disease prediction using diverse machine learning and deep learning techniques. Several studies reviewed the recent advancements and limitations of applying machine learning for cardiovascular disease detection [10,[33][34][35][36]. For instance, the studies [8,[37][38][39][40] proposed different data mining and machine learning methods based on heartbeat segmentation and selection process, ECG images, images of carotid arteries, and others.…”

Section: Discussion On the Research Limitationsmentioning

confidence: 99%

“…By investigating the effectiveness of hybrid models combining different techniques, various researchers have explored diverse methodologies, including neural networks and various machine learning methods, to enhance prediction accuracy [3][4][5][6][7][8][9][10][11][12]. While these studies provide valuable insights, the variability in datasets, models, and outcomes underscores the complexity of the predictive task.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola,

Saeed,

Basurra

et al. 2024

Diagnostics

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Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.

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Section: Discussion On the Research Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola,

Saeed,

Basurra

et al. 2024

Diagnostics

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Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

Xia,

Innab,

Kandasamy

et al. 2024

Sci Rep

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To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min–max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min–max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

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Energy Efficiency of IoT Networks for Environmental Parameters of Bulgarian Cities

et al. 2022

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Building modern Internet of Things (IoT) systems is associated with a number of challenges. One of the most significant among them is the need for wireless technology, which will serve to build connectivity between the individual components of this technology. In the larger cities of Bulgaria, measures to ensure low levels of harmful emissions, reduce noise levels, and ensure comfort in urban environments have been taken. LoRa technology shows more advantages in transmission distance and low energy consumption compared to other technologies. That is why this technology was chosen for the design of wireless sensor networks (WSN) for six cities in Bulgaria. These networks have the potential to be used in IoT configurations. Appropriate modules and devices for building WSN for cities in Bulgaria have been selected. It has been found that the greater number of nodes in the WSN leads to an increase in the average power consumed in the network. On the other hand, depending on the location of these nodes, the energy consumed may decrease. The performance of wireless sensor networks can be optimized by applying appropriate routing protocols, which are proposed in the available literature. The methodology for energy efficiency analysis of WSN can be used in the design of wireless sensor networks to determine the parameters of the environment, with the possibility of application in IoT.

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Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Cited by 5 publications

References 80 publications

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

Energy Efficiency of IoT Networks for Environmental Parameters of Bulgarian Cities

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