Volume 10 • Issue 2 • PP: 66-71 • 2024
Spatial Convolution Splines for Multivariate Spatial Data
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The Spatial Convolution Splines Multivariate Regression Model (SCSMRM) were used on the data represented a diabetes disease measurements across different regions in Iraq (Basrah, Baghdad, Babylon, Sulaimanya) while considering multiple risk factors such as age, BMI, weight , income, education level, blood pressure for the same geographic location for (200) patient, and combine the health data with the risk factor data to create a comprehensive dataset. Each record in the dataset should include the geographic location, diabetes status, and values for each risk factor we applied (SCSMRM), the results showed that significant the model and the risk factors studied in the model explain 61% of the changes that occur in the diabetes. It also showed the significance of the factors (age - weight - body mass index (BMI) - educational level - blood pressure) and the non-significance of the variable (income), and these results are consistent with the actual reality of the disease.
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