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Farzammehr, Mohadeseh, McLachlan, Geoffrey. (1403). Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests. , 36(1), 39-49. doi: 10.22059/jsciences.2025.397759.1007936
Mohadeseh Farzammehr; Geoffrey J McLachlan. "Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests". , 36, 1, 1403, 39-49. doi: 10.22059/jsciences.2025.397759.1007936
Farzammehr, Mohadeseh, McLachlan, Geoffrey. (1403). 'Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests', , 36(1), pp. 39-49. doi: 10.22059/jsciences.2025.397759.1007936
Farzammehr, Mohadeseh, McLachlan, Geoffrey. Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests. , 1403; 36(1): 39-49. doi: 10.22059/jsciences.2025.397759.1007936

Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests

Journal of Sciences, Islamic Republic of Iran
دوره 36، شماره 1، فروردین 2025، صفحه 39-49    اصل مقاله (325.67 K)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22059/jsciences.2025.397759.1007936
نویسندگان
Mohadeseh Farzammehr* 1؛ Geoffrey J McLachlan2
11 Judiciary Research Institute, Tehran, Islamic Republic of Iran
22 Department of Mathematics, University of Queensland, Brisbane, Australia
چکیده
With the growing utilization of advanced machine-learning techniques, such as random forests, understanding the significance of spatial factors within these models is increasingly imperative. This study proposes a novel approach to develop spatially explicit classification random forest models by integrating spatially lagged variables, mirroring various spatial panel data econometric specifications. We assess the comparative performance of these models against traditional spatial and non-spatial regression methods to predict suicide mortality rates across 31 provinces in Iran, utilizing data from 2011 to 2021. Results reveal that the spatial random forest model, incorporating spatial lag parameters, achieves a remarkable accuracy of 89.19% in predicting suicide mortality levels, surpassing traditional spatial econometric models (46.51%) and non-spatial random forest models (27.03%). While highlighting the effectiveness of spatial random forest models with spatial lag parameters, this study also recognizes the continued relevance of traditional spatial econometric models in predicting suicide mortality rates. These findings offer valuable insights into the interplay between spatial considerations and predictive modeling, providing essential guidance for researchers in selecting appropriate models for spatial data analysis.
کلیدواژه‌ها
Spatial Econometrics models؛ Machine-Learning Techniques؛ Random Forests؛ Iran
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