Enhancing Patient Outcomes with AI: Early Detection of Esophageal Cancer in the USA

Abstract

Esophageal cancer is deemed one of the most aggressive malignancies, with only a 20% five-year survival rate for patients diagnosed in advanced stages. It is approximated that more than 20,000 new cases are diagnosed each year in the United States, accounting for over 16,000 deaths annually. The main goal of this study was to develop and validate AI algorithms for the early detection of esophageal cancer using advanced machine learning techniques, and analyzing data from medical imaging, EHRs, and genomic profiles. The dataset used in this work on esophageal cancer is an aggregate of patient records from various reliable repositories, including but not limited to hospital EHRs, publicly available cancer registries, and specialized medical databases such as SEER. It included key variables of the demographic information: age, sex, and race; clinical history such as comorbidities, symptoms, and risk factors like GERD and Barrett's esophagus; diagnostic data, which includes imaging results, histopathology, and biomarkers; and treatment outcome data, including surgical procedures, chemotherapy regimens, and survival rate. Among the selected algorithms are Logistic Regression, Random Forest, and XG-Boost. Random Forest and XG-Boost classifiers did extremely well, achieving high accuracy, perfect precision, recall, and an F1-score for each class, which ascertains how much better these models classify instances perfectly without mistakes. The integration of AI-driven early detection technologies has deep implications for the US healthcare system, especially in improving patient outcomes. Early detection of diseases through predictive modeling can lead to timely interventions that are often crucial in improving prognosis and treatment efficacy.