Machine learning modelling and explainability of coronary heart disease based on Mediterranean diet DOI
Declan Ikechukwu Emegano, Abraham Ayobamiji Awosusi,

Yannick Meupeu Wouanche

и другие.

Mediterranean Journal of Nutrition and Metabolism, Год журнала: 2025, Номер unknown

Опубликована: Май 22, 2025

Background Coronary heart disease (CHD) occurs due to the narrowing or blockage of coronary arteries caused by atherosclerosis. It is one leading factors widespread mortality and morbidity. The latest research highlighted importance Mediterranean diet (MD) as an excellent cardioprotective nutritional regimen because its abundant content monounsaturated fats, antioxidant-rich compounds, anti-inflammatory nutrients. Conventional CHD risk models frequently overlook food habits, highlighting need for sophisticated predictive modeling that includes lifestyle aspects. Objectives We aim use machine learning (ML) prediction combining adherence MD with clinical characteristics. Method For present study, we employed Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP) Classifier, Gaussian Naive Bayes (GNB) on dataset overall diversity cumulative preventive effects against CHD. was published 26 April 2021 Mendeley. Result results, shown in this indicate RF performed excellently 0.90, 0.95, 0.90 accuracy, precision, recall, F-1 score values, respectively. Shapley additive explanations (SHAP) Local Interpretable Model-agnostic Explanations (LIME) showed Glucose, high-density lipoprotein cholesterol (HDL-C), bread, chocolate have a high impact prediction. Conclusion ML great potential has

Язык: Английский

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach DOI Creative Commons

Nermeen Gamal Rezk,

Samah Alshathri,

Amged Sayed

и другие.

Bioengineering, Год журнала: 2024, Номер 11(10), С. 1016 - 1016

Опубликована: Окт. 12, 2024

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult grasp how the “black boxes”, or non-interpretable models, behave inside. Predicting disease crucial healthcare, since allows prompt diagnosis and treatment of patient’s true state. Nonetheless, forecast illness with any degree accuracy. In this study, we have suggested a framework prediction based on Explainable artificial intelligence (XAI)-based hybrid such as LightBoost XGBoost algorithms. The main goals are build predictive models apply SHAP (SHapley Additive expPlanations) LIME (Local Interpretable Model-agnostic Explanations) analysis improve interpretability models. We carefully construct our systems test different ensemble learning algorithms determine which model best (HDP). approach promotes transparency when examining these widespread health issues. By combining XAI, important factors risk signals that underpin co-occurrence made visible. accuracy, precision, recall were evaluate their efficacy. This study highlights healthcare be transparent recommends inclusion XAI medical decisionmaking.

Язык: Английский

Процитировано

8
Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project DOI Creative Commons
Hanna Kwiendacz,

Bi Huang,

Yang Chen

и другие.

Cardiovascular Diabetology, Год журнала: 2025, Номер 24(1)

Опубликована: Фев. 15, 2025

Язык: Английский

Процитировано

1
Novel framework of significant risk factor identification and cardiovascular disease prediction DOI
Soham Bandyopadhyay,

A Samanta,

Monalisa Sarma

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 263, С. 125678 - 125678

Опубликована: Ноя. 12, 2024

Язык: Английский

Процитировано

3
Recent trends in diabetes mellitus diagnosis: an in-depth review of artificial intelligence-based techniques DOI
Salman Khalid, Hojun Kim, Heung Soo Kim

и другие.

Diabetes Research and Clinical Practice, Год журнала: 2025, Номер unknown, С. 112221 - 112221

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Machine learning modelling and explainability of coronary heart disease based on Mediterranean diet DOI
Declan Ikechukwu Emegano, Abraham Ayobamiji Awosusi,

Yannick Meupeu Wouanche

и другие.

Mediterranean Journal of Nutrition and Metabolism, Год журнала: 2025, Номер unknown

Опубликована: Май 22, 2025

Background Coronary heart disease (CHD) occurs due to the narrowing or blockage of coronary arteries caused by atherosclerosis. It is one leading factors widespread mortality and morbidity. The latest research highlighted importance Mediterranean diet (MD) as an excellent cardioprotective nutritional regimen because its abundant content monounsaturated fats, antioxidant-rich compounds, anti-inflammatory nutrients. Conventional CHD risk models frequently overlook food habits, highlighting need for sophisticated predictive modeling that includes lifestyle aspects. Objectives We aim use machine learning (ML) prediction combining adherence MD with clinical characteristics. Method For present study, we employed Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP) Classifier, Gaussian Naive Bayes (GNB) on dataset overall diversity cumulative preventive effects against CHD. was published 26 April 2021 Mendeley. Result results, shown in this indicate RF performed excellently 0.90, 0.95, 0.90 accuracy, precision, recall, F-1 score values, respectively. Shapley additive explanations (SHAP) Local Interpretable Model-agnostic Explanations (LIME) showed Glucose, high-density lipoprotein cholesterol (HDL-C), bread, chocolate have a high impact prediction. Conclusion ML great potential has

Язык: Английский

Процитировано

0