Articles

Explainable AI for Foreign Direct Investment Analysis: Evidence from Central Asia

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Foreign direct investment (FDI) is an important factor in the economic development of Central Asian countries, where investment flows have traditionally been concentrated in resource-based sectors. In the context of a growing focus on diversification, the need to analyze and study the determinants of FDI is increasing.

This study examines the determinants of FDI inflows in Central Asian countries using machine learning methods (CatBoost) and explainable artificial intelligence (SHAP), and compares the results with a classical econometric approach based on a two-way fixed effects (TWFE) model. Given the limited availability of data, a transfer learning approach is applied: the model is first trained on a group of countries structurally similar to Central Asia and then fine-tuned on the regional sample.

The results show that key macroeconomic factors such as Trade (% of GDP), Current account balance (% of GDP), and several other macroeconomic variables remain significant across both methodologies. At the same time, ML identifies additional regional patterns, such as a higher importance for FDI of determinants including Adjusted savings: carbon dioxide damage (% of GNI), Urban population (% of total population), and Access to electricity (% of population), among others.

The findings indicate that XAI provides interpretable results that are consistent with classical methods and additionally allows for capturing nonlinearities and regional heterogeneity. The study extends the application of ML and XAI in data-constrained Central Asian settings and demonstrates the value of combining econometric and machine learning approaches in the analysis of FDI determinants.

An Explainable Artificial Intelligence (XAI) Methodology for Heart Disease Classification

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Heart disease continues to be one of the predominant contributors to morbidity and mortality on a global scale, underscoring the imperative for early and precise diagnosis to enhance patient outcomes. Machine Learning (ML) has emerged as a formidable instrument in the classification of cardiovascular diseases, utilizing intricate clinical datasets to discern patterns that conventional statistical methodologies may fail to detect. Nevertheless, notwithstanding their robust predictive capabilities, numerous machine learning models function as black-box systems, exhibiting a deficiency in transparency regarding their decision-making processes. The absence of interpretability presents a considerable challenge in clinical environments, where trust, accountability, and elucidation are of utmost importance for medical professionals. In order to tackle this issue, we propose a methodology for heart disease classification that is grounded in Explainable Artificial Intelligence (XAI). This approach incorporates interpretable machine learning models to improve diagnostic transparency and reliability. Our framework conducts an evaluation of various classifiers, including Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and LightGBM. This assessment is based on essential performance metrics, namely accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) have been integrated to enhance the interpretability of the model. The experimental findings indicate that XGBoost surpasses alternative models, attaining the highest classification accuracy of 92% and an AUC-ROC score of 0.93, all while preserving interpretability. This study underscores the significance of incorporating Explainable Artificial Intelligence (XAI) techniques within medical AI applications. It advocates for the adoption of transparent, interpretable, and clinically dependable machine learning methodologies to enhance clinical decision-making and optimize patient outcomes.