An exhaustive review of machine and deep learning based diagnosis of heart diseases

Rath, Adyasha; Mishra, Debahuti; Panda, Ganapati; Satapathy, Suresh Chandra

doi:10.1007/s11042-021-11259-3

Cited by 14 publications

(9 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The review of Rath et al [ 28 ] included methods for feature extraction, selection, and reduction, as well as machine learning-/deep learning-based classification schemes, CVD data sets, and types of heart disease. They also listed some heart disease attributes identified in the 60 collected papers.…”

Section: Related Surveysmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

View full text Add to dashboard Cite

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

show abstract

Section: Related Surveysmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…Shah et al counted 2634 patents linking CVDs to the ECG signal [ 69 ]. Machine learning and data mining techniques [ 69 , 70 ] and deep learning [ 71 ] are widely used for automatic diagnosis based on the ECG signal.…”

Section: Limitationsmentioning

confidence: 99%

Cardiovascular diseases diagnosis by impedance cardiography

Mansouri

Alharbi

Alshrouf

et al. 2022

Journal of Electrical Bioimpedance

View full text Add to dashboard Cite

Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.

show abstract

“…Wilson and colleagues [6]. Many researchers then develop machine learning and deep learning methods in the subsequent phases using the datasets from the UCI repository in order to predict cardiovascular disease [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: International Journal For Multidisciplinary Research (Ijfmr)mentioning

confidence: 99%

“…However, the practise of feature selection in prediction models [14,16,17,19] has not only increased accuracy but also mitigated problems such increased processing costs and overfitting brought on by irrelevant input features during the learning process. In addition, the approaches may also present problems with the design, which may be solved with the assistance of the most appropriate advanced prediction models within the framework of a prospective research project.…”

Section: International Journal For Multidisciplinary Research (Ijfmr)mentioning

confidence: 99%

A Comprehensive Survey on Heart Disease Prediction

Singh¹,

Arora²

2022

IJFMR

View full text Add to dashboard Cite

Heart disease, stroke, and other vascular illnesses will kill one-third of humanity, according to the World Health Organization. Reducing mortality rates and providing the best clinical decision support for cardiac patients necessitates the use of an appropriate machine learning model to target early detection and accurate heart disease prediction. In this study, the Cleveland and Z-Alizadeh Sani datasets are used to examine the efficacy of several heart disease prediction algorithms.

show abstract

An exhaustive review of machine and deep learning based diagnosis of heart diseases

Cited by 14 publications

References 58 publications

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cardiovascular diseases diagnosis by impedance cardiography

A Comprehensive Survey on Heart Disease Prediction

Contact Info

Product

Resources

About