Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study
Notice bibliographique
Résumé
Background: Diabetic nephropathy (DN), a severe complication of diabetes, is characterized by proteinuria, hypertension, and progressive renal function decline, potentially leading to end-stage renal disease. The International Diabetes Federation projects that by 2045, 783 million people will have diabetes, with 30%-40% of them developing DN. Current diagnostic approaches lack sufficient sensitivity and specificity for early detection and diagnosis, underscoring the need for an accurate, interpretable predictive model to enable timely intervention, reduce cardiovascular risks, and optimize health care costs. Objective: This study aimed to develop and validate a machine learning-based predictive model for DN in patients with type 2 diabetes, with a focus on achieving high predictive accuracy while ensuring transparency and interpretability through explainable artificial intelligence techniques, thereby supporting early diagnosis, risk assessment, and personalized clinical decision-making. Methods: Our retrospective cohort study investigated 1000 patients with type 2 diabetes using data from electronic medical records collected between 2015 and 2020. The study design incorporated a sample of 444 patients with DN and 556 without, focusing on demographics, clinical metrics such as blood pressure and glucose levels, and renal function markers. Data collection relied on electronic records, with missing values handled via multiple imputation and dataset balance achieved using Synthetic Minority Oversampling Technique (SMOTE). In this study, advanced machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), CatBoost, and Light Gradient-Boosting Machine (LightGBM), were used due to their robustness in handling complex datasets. Key metrics, including accuracy, precision, recall, F1-score, specificity, and area under the curve, were used to provide a comprehensive assessment of model performance. In addition, explainable machine learning techniques, such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), were applied to enhance the transparency and interpretability of the models, offering valuable insights into their decision-making processes. Results: XGBoost and LightGBM demonstrated superior performance, with XGBoost achieving the highest accuracy of 86.87%, a precision of 88.90%, a recall of 84.40%, an F1-score of 86.44%, and a specificity of 89.12%. LIME and SHAP analyses provided insights into the contribution of individual features to elucidate the decision-making processes of these models, identifying serum creatinine, albumin, and lipoproteins as significant predictors. Conclusions: The developed machine learning model not only provides a robust predictive tool for early diagnosis and risk assessment of DN but also ensures transparency and interpretability, crucial for clinical integration. By enabling early intervention and personalized treatment strategies, this model has the potential to improve patient outcomes and optimize health care resource usage.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».