Galoitica: Journal of Mathematical Structures and Applications
Journal DOI
2834-5568ISSN (Online)
Volume 10 , Issue 2 , PP: 66-71, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Sackineh Shamil Jasim 1 *
Doi: https://doi.org/10.54216/GJMSA.0100207
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.
Spatial , Convolution , Splines , Regression , Multivariate Spatial Data.
[1] Majumdar A, Paul D, Bautista D. A generalized convolution model for multivariate nonstationary spatial processes. Stat Sin 2010:675–95.
[2] Kleiber W, Nychka D, Bandyopadhyay S. A model for large multivariate spatial data sets. Stat Sin 2019;29:1085–104.
[3] Davies ER. Computer vision: principles, algorithms, applications, learning. Academic Press; 2017.
[4] Chen G, Guo Y, Zeng Q, Zhang Y. A Novel Cellular Network Traffic Prediction Algorithm Based on Graph Convolution Neural Networks and Long Short-Term Memory through Extraction of Spatial-Temporal Characteristics. Processes 2023;11:2257.
[5] Sun Y, Pang S, Zhang J, Zhang Y. Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer). Phys Fluids 2024;36.
[6] Micula G, Micula S. Handbook of splines. vol. 462. Springer Science & Business Media; 2012.
[7] Schumaker LL. Spline functions: computational methods. SIAM; 2015.
[8] Chui CK, Ron A. On the convolution of a box spline with a compactly supported distribution: linear independence for the integer translates. Can J Math 1991;43:19–33.
