Why the study?
Most prior studies used existing datasets rather than IoT sensor data, which present challenges with noise and missing values when continuously predicting and monitoring heart disease.
Design
Review
Key result
Machine learning algorithms combined with IoT technology for continuous heart disease prediction face challenges in processing noisy sensor data compared to standard datasets.
Authors
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Noisy sensor data may limit ML-IoT prediction reliability; leaves open robust preprocessing frameworks before clinical testing.
This review highlights the challenges and potential frameworks for using machine learning and IoT technology for continuous ECG monitoring and heart disease prediction.
He et al. (2020) conducted a review in Heart disease. Machine learning based on IoT technology was evaluated. Machine learning algorithms combined with IoT technology for continuous heart disease prediction face challenges in processing noisy sensor data compared to standard datasets.