Leveraging data-driven insights for esophageal and gastric cancer diagnosis.

BACKGROUND: Oesophago-Gastric Cancer (OGC) and its precursor, high-grade dysplasia (HGD), provide a significant health concern owing to their rapid development and often late diagnosis. Timely identification of individuals is crucial to avert illness advancement and enhance clinical results. Nonetheless, there has been little research on machine learning models and effective statistical techniques for predicting OGC risk. AIMS: This research seeks to uncover risk variables linked to HGD and OGC and to assess the prediction efficacy of several machine learning models using diverse patient data. METHODS: This research used a comprehensive methodology that integrates specialist analysis, statistical modeling, and data visualization. Thorough preprocessing and risk factor identification were conducted for the creation of regression models to forecast OGC and HGD outcomes. Moreover, visual analytics and interactive dashboards were used to improve interpretability and clinical significance. RESULTS: The evaluation of the models revealed that the linear regression model exhibited higher performance, with a Mean Squared Error (MSE) of 0.0244, a Mean Absolute Error (MAE) of 0.116, and an R-squared value of 0.046. CONCLUSION: Our results demonstrate the efficacy of machine learning models, namely linear regression, as a dependable instrument for the early risk assessment of OGC and HGD. The integration of predictive modeling, statistical analysis, and dashboarding provides a robust framework that assists healthcare practitioners in diagnosis, treatment, and patient monitoring.