Key result
Stacking ensemble framework outperforms 10 single classifiers in CVD prediction accuracy and applicability.
Why the study?
Existing traditional machine learning models do not handle data inequities well and have relatively low prediction accuracy for early outcomes in cardiovascular disease.
Does an ensemble framework based on stacking model fusion improve prediction accuracy for cardiovascular disease compared to single classifier models?
Does an ensemble framework based on stacking model fusion improve prediction accuracy for cardiovascular disease compared to single classifier models?
An ensemble machine learning framework using stacking model fusion improves the accuracy of cardiovascular disease prediction compared to traditional single classifiers.
Answers are grounded in this paper and cite their sources.
May support refined CVD prediction models; leaves open external validation before clinical use.
The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability.
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Liu et al. (2022) studied Cardiovascular disease. Ensemble framework based on stacking model fusion vs. 10 single classifier models was evaluated on Prediction accuracy and applicability. An ensemble framework based on stacking model fusion outperformed 10 single classifier models in terms of accuracy and applicability for predicting cardiovascular disease.
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