Title: Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings,
Abstract: Over 150,000 new people in the United States are diagnosed with colorectal
cancer each year. Nearly a third die from it (American Cancer Society). The
only approved noninvasive diagnosis tools currently involve fecal blood count
tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five
minutes and are available over the counter for as low as \$15. They are highly
specific, yet not nearly as sensitive, yielding a high percentage (25%) of
false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too
generalized, meaning that a positive result could mean much more than just
colorectal cancer, and could just as easily mean hemorrhoids, anal fissure,
proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer,
rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis
from radiation therapy, and others. Stool DNA tests, the modern benchmark for
CRC screening, have a much higher sensitivity and specificity, but also cost
\$600, take two weeks to process, and are not for high-risk individuals or
people with a history of polyps. To yield a cheap and effective CRC screening
alternative, a unique ensemble-based classification algorithm is put in place
that considers the FIT result, BMI, smoking history, and diabetic status of
patients. This method is tested under ten-fold cross validation to have a .95
AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once
clinically validated, this test promises to be cheaper, faster, and potentially
more accurate when compared to a stool DNA test.