Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees DOI Creative Commons
Dominika Gajdosikova, Jakub Michulek

Agriculture, Год журнала: 2025, Номер 15(10), С. 1077 - 1077

Опубликована: Май 16, 2025

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually most important indicator distress. As agriculture capital-intensive sector with high reliance on borrowed funds, firms in this more vulnerable to insolvency. This study examines performance artificial neural networks (ANNs) decision trees (DTs) predicting bankruptcy Slovak enterprises. In an attempt compare models’ performances, consequential ratios investigated through machine learning approaches. ANN DT models found perform significantly better than traditional forecast methods. achieved AUC 0.9500, accuracy 96.37%, precision 96.60%, recall 99.68%, F1-score 98.12%, determining its robust predictive ability. performed little (0.9550) 97.78%, 98.69%, 99.01%, 98.85%, ability interpretability. These findings confirm potential for applying AI-based enhance risk assessment. provides informative results analysts, policymakers, corporate managers support early intervention strategies. Additional research would be required explore state-of-the-art AI techniques further refine forecasting decision-making sectors like agriculture.

Язык: Английский

Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees DOI Creative Commons
Dominika Gajdosikova, Jakub Michulek

Agriculture, Год журнала: 2025, Номер 15(10), С. 1077 - 1077

Опубликована: Май 16, 2025

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually most important indicator distress. As agriculture capital-intensive sector with high reliance on borrowed funds, firms in this more vulnerable to insolvency. This study examines performance artificial neural networks (ANNs) decision trees (DTs) predicting bankruptcy Slovak enterprises. In an attempt compare models’ performances, consequential ratios investigated through machine learning approaches. ANN DT models found perform significantly better than traditional forecast methods. achieved AUC 0.9500, accuracy 96.37%, precision 96.60%, recall 99.68%, F1-score 98.12%, determining its robust predictive ability. performed little (0.9550) 97.78%, 98.69%, 99.01%, 98.85%, ability interpretability. These findings confirm potential for applying AI-based enhance risk assessment. provides informative results analysts, policymakers, corporate managers support early intervention strategies. Additional research would be required explore state-of-the-art AI techniques further refine forecasting decision-making sectors like agriculture.

Язык: Английский

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