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

Authors

• Chih-Hao Chang, National Central University
• Mei-Ling Chan, Jiaying University
• Yu-Hung Fang, Chiayi Chang-Gung Memorial Hospital
• Po-Lin Huang, Feng Chia University
• Tsung-Yi Chen, Feng Chia University
• Tsun-Kuang Chi, Ming Chi University of Technology
• I Elizabeth Cha, CYCU, Chung Yuan Christian University, Taiwan
• Tzong-Rong Ger, National Yang Ming University
• Kuo-Chen Li, CYCU, Chung Yuan Christian University, Taiwan
• Shih-Lun Chen, CYCU, Chung Yuan Christian University, Taiwan
• Liang-Hung Wang, Fuzhou University
• Jia-Ching Wang, National Central University
• Patricia Angela R. Abu, Ateneo de Manila UniversityFollow

Document Type

Article

Publication Date

4-18-2026

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.

Recommended Citation

Chang, Chih-Hao; Chan, Mei-Ling; Fang, Yu-Hung; Huang, Po-Lin; Chen, Tsung-Yi; Chi, Tsun-Kuang; Cha, I Elizabeth; Ger, Tzong-Rong; Li, Kuo-Chen; Chen, Shih-Lun; Wang, Liang-Hung; Wang, Jia-Ching; and Abu, Patricia Angela R., (2026). Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks. Archīum.ATENEO.
https://archium.ateneo.edu/discs-faculty-pubs/444