Analysis of Regional Differences in Living Standards and Economic Growth using Neuro-Explicit Clustering Algorithms (On the Example of Kashkadarya Region)

Second, the relative levels of living standards and economic growth may be more nonlinear than what a standard model can depict. The complexity of the temporal and spatiotemporal nature of the data has spurred researchers to use advanced modeling approaches, such as Fuzzy C-Means (FCM) clustering and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which are known for their capacity to handle nonlinearity and multidimensionality of the data. The relationship between living standards in the context of economic growth is discussed in this study, including an example of Kashkadarya region of Uzbekistan for 2010–2025. While previous studies indicate that income, infrastructure, and virtual networks have large impacts on regional growth, the nature of these relationship is ambiguous, particularly in light of rapid change (via digital transformation). Digital infrastructure is proven to foster growth, but no thorough analysis have employed advanced modeling methods to gauge the extent of interaction between diversified socio-economic factors like income and digital infrastructure. Most previous studies used linear methods and ignored the complex nature of regional development. Now, based on FCM clustering and ANFIS model, this study intends to reveal the nonlinear relationships among income, infrastructure and digital infrastructure along with this kind of economic growth in the Kashkadarya region. Based on this analysis, the paper identified three distinct stages of digital infrastructure development across the region, and showed that digital infrastructure is an important driver of economic growth, especially when combined with increasing levels of income. The relationship between these variables was successfully modeled with good agreement with an R² of 0.977 by ANFIS. This study is novel in applying ANFIS to capture the multifaceted, non-linear interdependencies between socio-economic variables in a developing region. The results indicate that a regional perspective should be taken when first developing appropriate strategies, with digital structures needing to be prioritised. These figures can assist policymakers in focusing their interventions toward sustainable economic growth in other comparable regions.