What is the clinical evidence from this study?

Study design: Systematic Review. Population: Cardiovascular diseases (n=164). Intervention: IoT, IoMT, and machine learning. Primary outcome: Accuracy of machine learning techniques for cardiovascular disease prediction.

August 9, 2023Open Access

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

Key Result

Machine learning techniques, particularly neural networks, achieved over 90% accuracy in predicting and monitoring cardiovascular diseases using IoT and IoMT technologies.

Study Design

Type

Systematic Review (n=164)

Structured PICO

Population

164 high-impact journal papers focusing on cardiovascular disease detection and prediction using IoT/IoMT technologies and machine learning techniques

Intervention

IoT, IoMT, and machine learning techniques (including neural networks, random forest, XGBoost, k-NN, and SVM)

Outcome

Accuracy of cardiovascular disease detection, prediction, or monitoring

Machine learning and IoT/IoMT technologies show high accuracy (over 90% for neural networks) in predicting and monitoring cardiovascular diseases, though progress is limited by a lack of public datasets.

Limitations

Lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction

Abstract

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.

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