Develop a Risk Prediction Model for Phthalate-ester-induced Diseases

Using Machine Learning Algorithms to Develop a Risk Prediction Model for Phthalate-ester-induced Diseases and Suggestions for Improved Nursing Assessment

This exploratory study collected basic demographic and laboratory data from the Taiwan Biobank using artificial intelligence algorithms and applied data mining to identify the correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle, and disease.

This study was designed as exploratory research. The Institutional Review Board and the Taiwan Biobank approved the study before it was conducted (Approval Number: TWBR11007-06). The data set included information from participants between 30 and 70 years old who were tested for PAEs between 2016 and 2022 (1337 cases). The data set includes (1) questionnaire responses, such as basic personal information, individual health behaviors, and female health issues; (2) physical examination results, such as body mass index (BMI), body fat percentage, waist circumference, hip circumference, waist-to-hip ratio, blood pressure, heart rate, pulmonary function, and bone mineral density; (3) blood and urine analyses results from blood tests, serology tests, hepatobiliary function tests, renal function tests, and urinalysis; and (4) data on PAE content of urine

inclusion:

1. The target subjects of this study are individuals aged 30 to 70 years old
2. who were tested for PAEs from January 1, 2016 to December 31, 2020.

exclusion:

1who have not been tested for plasticizers