Abstract 1973: A geo inelegant approach to cancer disparity
Authors: Metwaly Ahmed Fadiel , Koster Adam Thomas , Abbasi Mohammad , Hassouneh Aya , Tiro Jasmin , Lee Nita , Odunsi Adekunle 
Published: April 2023

Abstract
UCRShiny is an intelligent user interface application that utilizes R and R-Shiny tools and statistical intelligence to allow users to visualize geospatial cancer data and perform advanced analyses and predictive modeling. UCRShiny's focus is the catchment area (in Illinois and Indiana) near UCCC and utilizes various metadata. This includes fitting univariate and multivariate models to make statistically sound inferences from the data. It also allows the user to plot specific data on a map of the catchment area as layers for descriptive analysis and visual geospatial assessment. As well as assessing the significance of the relation between these factors. These statistical tools should have a level of intelligence that can determine the correctness of the model's assumptions. An advantage of our tool is that it provides an overall diagnosis of the model to the user without the need for the intervention of an expert statistician. The data selected for analysis are assessed to ensure they meet the assumptions of the statistical model being used, and proper data transformations are applied. The presence of confounding factors is assessed using linear models in sequence. Initially, a single variable linear model is fit using the primary variable of interest (i.e., Cancer mortality) as the response variable and the second variable in the univariate analysis as the covariate. The second model adds the internal metadata columns as potential confounders to the model. In the third step, the percent change in the coefficient of the second variable in the first and second linear models is calculated. Multivariate analysis using ML is used to simultaneously assess the effect of multiple variables on the outcome variable. Here, we use various ML algorithms based on the problem and data at hand, report the significant features, and quantify their impact on the response of interest. The tool allows users to utilize the in-app data, load their dataset, or combine both datasets. It helps to visualize geospatial health data on a map, perform statistical analysis and build ML/AI models without being concerned about the validity of the statistical models. After uploading data and selecting the appropriate statistical test/analysis, results will be visualized as a plot on a map with the specified layers collared by the layer variable values. The layers can be turned on or off using specific buttons on the map. GIS maps may be easily shared and included in apps and are available to almost everyone, anywhere. The user can study the effect of multiple variables and rank their importance in predicting the response variable using multivariate ML/AI models. The UI allows the user to select an appropriate ML algorithm for the model or the application's intelligent backend algorithms to choose the proper ML model (This will be implemented in phase II). The outcome feature and the covariates to use in the model can be selected using searchable textboxes in the UI, and the model is run using the Run model button.
Citation Format: Ahmed Fadiel Metwaly, Adam Thomas Koster, Mohammad Abbasi, Aya Hassouneh, Jasmin Tiro, Nita Lee, Adekunle Odunsi. A geo inelegant approach to cancer disparity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1973.