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. 2024 Dec 29;13(1):10.
doi: 10.1007/s13755-024-00324-4. eCollection 2025 Dec.

Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments

Hao-Ting Wu  1 , Chien-Chang Liao  2 , Chiung-Fang Peng  3 , Tso-Ying Lee  4 , Pei-Hung Liao  5
Affiliations

Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments

Hao-Ting Wu et al. Health Inf Sci Syst. .

Abstract

Background: Health risks associated with phthalate esters depend on exposure level, individual sensitivities, and other contributing factors.

Purpose: This study employed artificial intelligence algorithms while applying data mining techniques to identify correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle factors, and disease outcomes.

Methods: We conducted exploratory analysis using demographic and laboratory data collected from the Taiwan Biobank. The study developed a prediction model to examine the relationship between phthalate esters and the risk of developing certain diseases based on various artificial intelligence algorithms, including logistic regression, artificial neural networks, and Bayesian networks.

Results: The results indicate that phthalate esters exhibited a greater impact on bone and joint issues than heart problems. We observed that DEHP metabolites, such as mono(2-carboxymethylhexyl) phthalate, mono-n-butyl phthalate, and monoethylphthalate, leave higher residue in females than in males, with statistically significant differences. Monoethylphthalate levels were lower in individuals who exercised regularly than those who did not, indicating statistically significant differences.

Conclusions: This study's findings can serve as a valuable reference for clinical nursing assessments regarding diseases related to osteoporosis, arthritis, and musculoskeletal pain. Medical professionals can enhance care quality by considering factors beyond patients' essential physical assessment items.Trial Registration: This study was registered under NCT05892029 on May 5, 2023, retrospectively.

Keywords: Environmental toxic substances; Machine learning; Nursing assessments; Phthalate esters; Prediction model.

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Conflict of interest statement

Conflict of interest The authors have not disclosed any competing interests.

Figures

Fig. 1
Fig. 1
Study framework
Fig. 2
Fig. 2
Bayesian network: cardiomyopathies as an example
Fig. 3
Fig. 3
The area under the curve (AUC) of the three prediction models for musculoskeletal system disease risk factors and phthalate Esters
See this image and copyright information in PMC

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