iranian stomach cancer registry data analysed through multivariate adaptive geographically weighted generalized poisson regression spline

Introduction: in 2022, gastric and breast cancer had a mortality rate of 6.8 cases, ranking fourth despite intervention efforts. a 2020 study by the international agency for research on cancer found global variations in incidence rates. this study examines key risk factors and preventive measures [1-2].materials and methods: this applied ecological study uses the magwgprs (multivariate adaptive geographically weighted generalized poisson regression spline) model, integrating mars and gwgpr, to analyze cancer registry data. the model identifies geographic variations and hotspots in cancer risk. data sources include pathology reports, death records, biopsy data, and a non-communicable disease risk factor survey. the dataset comprises patient age, location, cancer case counts, and relevant risk factors. analysis is conducted using r, with arcgis for map visualization.results: according to the international agency for research on cancer, key risk factors for stomach cancer include obesity, smoking, physical inactivity, poor nutrition, age, and population density. the magwgprs model, a geographically weighted model, identifies regional variations in these factors by weighting observations based on distance using a kernel function and optimizing the model with the gcv criterion. our analysis highlights vegetable consumption, smoking, low physical activity, and age as the primary determinants of gastric cancer risk.conclusion: our model identifies vegetable consumption, smoking, low physical activity, and aging as significant risk factors for gastric cancer. further research is needed to refine obesity risk based on bmi criteria. the magwgprs model is a valuable tool for identifying high-risk regions, enabling targeted interventions and prioritizing key risk factors across diverse geographic areas.