Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses

Daisuke Nose, Tomokazu Matsui, Takuya Otsuka, Yuki Matsuda, Tadaaki Arimura, Keiichi Yasumoto, Masahiro Sugimoto, Shin-Ichiro Miura: “Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses,” Journal of Cardiovascular Development and Disease, Vol.10, No.7:291, pp.1-14, 2023.

Abstract

Background: Transthoracic impedance values have not been widely used to measure extravascular pulmonary water content due to accuracy and complexity concerns. Our aim was to develop a foundational model for a novel system aiming to non-invasively estimate the intrathoracic condition of heart failure patients.

Methods: We employed multi-frequency bioelectrical impedance analysis to simultaneously measure multiple frequencies, collecting electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers. Measurements were taken upon admission and after treatment, and longitudinal analysis was conducted.

Results: Using a light gradient boosting machine, and a decision tree-based machine learning method, we developed an intrathoracic estimation model based on electrical measurements and clinical findings. Out of the 286 features collected, the model utilized 16 features. Notably, the developed model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an area under the receiver characteristic curves (AUC) of 0.905 (95% CI: 0.870–0.940, p < 0.0001) in the cross-validation test. The accuracy significantly outperformed the conventional frequency-based method with an AUC of 0.740 (95% CI: 0.688–0.792, and p < 0.0001). Conclusions: Our findings indicate the potential of machine learning and transthoracic impedance measurements for estimating pleural effusion. By incorporating noninvasive and easily obtainable clinical and laboratory findings, this approach offers an effective means of assessing intrathoracic conditions.

Links

DOI: https://doi.org/10.3390/jcdd10070291

PDF: https://yukimat.jp/data/pdf/paper/PleuralEffusion_j_202307_nose_JCDD.pdf

BibTeX

code:references.bib

@article{bib:nose_PleuralEffusion_JCDD2023,

author={Nose, Daisuke and Matsui, Tomokazu and Otsuka, Takuya and Matsuda, Yuki and Arimura, Tadaaki and Yasumoto, Keiichi and Sugimoto, Masahiro and Miura, Shin-Ichiro},

title={Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses},

journal={Journal of Cardiovascular Development and Disease},

volume={10},

number={7:291},

pages={1--14},

year={2023},

url={https://www.mdpi.com/2308-3425/10/7/291},

doi={10.3390/jcdd10070291}

}

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