Room: Exhibit Hall | Forum 6
Purpose: Colorectal cancer (CRC) is third in prevalence and mortality among cancers in the US. Screening is recommended for ages 50-75 or anyone with a family history (FH) of CRC by the United States Preventative Services Task Force (USPSTF). However, since 1974 CRC has grown more prevalent in ages 18-49. Further, ages 50-75 is currently a large demographic. Thus, the aim of this study is to build robust machine learning models for more efficient risk-stratification.
Methods: The National Health Interview Survey (NHIS) and the Prostate, Lung, Colorectal, and Ovarian (PLCO) Screening Trial datasets contain 2,379 respondents whose first cancer was CRC and 280,669 never- cancer respondents. They were used for training and cross-testing 5 machine learning models: artificial neural network (ANN), naive Bayes (NB), linear discriminant analysis (LDA), support vector machine (SVM), and decision tree (DT). The modelsâ€™ predictors were age, body-mass index, smoking habits, Hispanic ethnicity, sex, race, and incidence of joint-aching/arthritis, emphysema, strokes, hypertension, coronary heart disease, myocardial infarction, liver comorbidity, diabetes, ulcers, and bronchitis. After training and cross-testing, the model with the highest concordance was used to stratify CRC risk into low, medium, and high risk groups.
Results: Among the 5 machine learning models, ANN had the highest concordance of 0.82 Â± .10, and gave sensitivity of 0.73 Â± 0.03, specificity of 0.78 Â± 0.04, positive predictive value of 0.17 Â± 0.04, and negative predictive value of 0.72 Â± 0.04. The total variance is due to the standard error and the error from cross-testing. Compared to USPSTF guidelines, the ANN misclassifies a lower percentage of the CRC/never-cancer populations as low/high-risk, respectively.
Conclusion: A multi-parameterized ANN was the top performer in scoring CRC risk based solely on personal health data. The trained ANN can be used to stratify individualâ€™s CRC risk for more effective screening and intervention.