What question did this study set out to answer?

This research aims to develop a robust model for predicting cardiac index using non-invasive methods and artificial neural networks.

April 22, 2026Open Access

Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks

Key Result

An artificial neural network using three physiological parameters from non-invasive IoT devices achieved 97.78% accuracy in predicting cardiac index.

Key Points

This research aims to develop a robust model for predicting cardiac index using non-invasive methods and artificial neural networks.
Integrated IoT sensing devices for data collection.
Utilized an artificial neural network for predictive modeling.
Implemented data preprocessing and model training to enhance performance.
Achieved a classification accuracy of 97.78% with three physiological parameters.
Maintained strong predictive performance even with two parameters.
Confirmed potential for real-time, non-invasive cardiac index assessment.

Structured PICO

Does an artificial neural network integrated with non-invasive IoT sensing devices improve cardiac index prediction accuracy compared to traditional approaches?

Intervention

Artificial neural network (ANN) integrated with non-invasive IoT sensing devices (TERUMO ES-P2000 blood pressure monitor and PhysioFlow PF07 Enduro cardiac hemodynamic analyzer) for cardiac index (CI) prediction

Comparator

Traditional approaches

Outcome

Classification accuracy of cardiac index (CI) predictionsurrogate

An artificial neural network integrated with non-invasive IoT sensing devices can accurately predict cardiac index, offering a potential framework for real-time, non-invasive hemodynamic assessment.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0.

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Cite This Study

Chang et al. (Sat,) reported a other. An artificial neural network using three physiological parameters from non-invasive IoT devices achieved 97.78% accuracy in predicting cardiac index.

synapsesocial.com/papers/69e8656e6e0dea528dde9f7c https://doi.org/https://doi.org/10.3390/bioengineering13040477

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