Machine Learning to Construct an Association Model for Lung Cancer and Environmental Hormone

Pei-Hung Liao128 enrolled

Overview

To apply machine learning to construct an association model regarding lung cancer and environmental hormones to more comprehensively identify factors that may lead to lung cancer and to improve existing lung cancer nursing assessments.

The present study is exploratory, and its design was divided into three stages: Stage 1 explored the environmental hormonal risk factors for lung cancer and constructed an association model using machine learning algorithms. Stage 2 validated the results of the association model with better predictive results in clinical settings. Stage 3 involved recommendations for reconstructing nursing assessments based on the results of the clinical model validation. The three stages are summarized as follows.

Study Type

OBSERVATIONAL

Enrollment

128

Conditions

Lung Neoplasms

Eligibility

Sex: ALLMin age: 20 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: 1. 20 years or older; 2. conscious, without mental disorders; 3. able to use Mandarin to communicate, had a smartphone or tablet computer and could operate it by themselves, 4. and were willing to participate in the study after the reasons for participation were explained and a consent form was signed, indicating their consent. Exclusion Criteria: * The exclusion criteria were those who could not understand the content of the nursing assessment questions and those with mental disorders.

Locations (2)

National Taipei university of nursing and health science

Taipei, Taiwan

National Taiwan University, Cancer Center

Taipei, Taiwan

Outcomes

Primary Outcomes

identify factors that may lead to lung cancer and environmental hormones.

This association model can assist in identifying the risk groups of lung cancer and advising physicians on the appropriate environmental hormone testing and treatment to provide nursing guidance and medical advice

Time frame: the database research samples from May 2014 to December 2020.

Data from ClinicalTrials.gov

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