Supplementary Datasets Files

<strong style="color:rgb(4, 25, 20);">TITLE: &lt;/strong&gt;A Method to Identify At-Risk Individuals When They Have Developed Colorectal Cancer by Machine Learning Analysis of Their Multiple Longitudinal Self-Reported Symptoms</strong><strong style="color:rgb(0, 0, 0);">ABSTRACT: &lt;em&gt;Background. &lt;/em&gt;&lt;/strong&gt;Surveillance of at-risk individuals and identification of those who have developed cancer could improve the treatment success and reduce mortality. Colorectal cancer (CRC) is the second most common cause of death worldwide. <strong style="color:rgb(0, 0, 0);">&lt;em&gt;Aim. &lt;/em&gt;&lt;/strong&gt;The purpose of this project is to demonstrate a new noninvasive surveillance regimen that could detect when individuals at-risk for colorectal cancer have developed early-stage colorectal cancer. This heuristic example is intended to encourage national health systems to begin collecting such surveillance data. <strong style="color:rgb(0, 0, 0);">&lt;em&gt;Method. &lt;/em&gt;&lt;/strong&gt;At-risk Individuals could include military veterans who were exposed to toxic environmental hazards and non-veterans who have a higher risk than the general population, as determined by the online Colorectal Cancer Risk Assessment Tool. Individuals would be instructed to self-report the frequency of the occurrence of multiple noninvasive symptoms each week. The recorded longitudinal data would be analyzed every two weeks by the machine learning classifier multiple logistic regression. <strong style="color:rgb(0, 0, 0);">&lt;em&gt;Results. &lt;/em&gt;&lt;/strong&gt;The frequency of occurrence of multiple symptoms in individuals would be compared with that of a reference population to stratify the individuals into two classes: those who have developed early-stage colorectal cancer with a high probability and those who have not. Individuals with a high probability of having developed early-stage colorectal cancer would be alerted and advised to seek medical attention. <strong style="color:rgb(0, 0, 0);">&lt;em&gt;Conclusion.&lt;/em&gt;&lt;/strong&gt;A noninvasive surveillance procedure is described that could alert at-risk individuals when they have a high probability of developing early-stage colorectal cancer. This is based upon the number of their self-reported multiple symptoms analyzed every two weeks using multiple logistic regression.</strong></strong></strong></strong></strong>