Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Mar 15, 2024)
Date Submitted: Oct 27, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Exploratory Study of Application of Machine Learning to Identify the Correlations between Phthalate Esters and Disease to Improve Nursing Assessment
ABSTRACT
Background:
The health risks of phthalate esters are determined by the amount of exposure, individual sensitivities, and promoting factors.
Objective:
Using artificial intelligence algorithms and applied data mining to identify the correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle, and disease
Methods:
This exploratory study collected basic demographic and laboratory data from the Taiwan Biobank. A prediction model for the relationship between phthalate esters and high disease risk was developed using several artificial intelligence algorithms, including logistic regression, an artificial neural network, and a Bayesian network.
Results:
The results revealed that phthalate esters have a greater influence on bone and joint problems, but a lesser impact on heart problems. On the other hand, the metabolites of DEHP, mono(2-carboxymethylhexyl) phthalate, mono-n-butyl phthalate, and monoethylphthalate, leave higher residues in females than in males, and the difference is statistically significant. Levels of monoethylphthalate are lower in people who exercise regularly than those who do not, and the difference is statistically significant.
Conclusions:
The results of this study can provide a reference for the clinical nursing assessment of diseases related to osteoporosis, arthritis, and musculoskeletal pain, and facilitate medical staff to consider factors other than patients' basic physical assessment items, thereby improving medical care quality. Clinical Trial: NCT05892029, retrospectively registered on 19/05/2023.
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