article · Bioengineering
Early and accurate diagnosis of cardiac disease is vital for patient care. A hybrid machine learning framework, designated ETCXGB, merges the ExtraTreeClassifier and XGBoost algorithms to enhance diagnostic accuracy. The technique operates by using the ExtraTreeClassifier to evaluate raw patient data and generate predicted probabilities. These probabilities are combined with the initial dataset to create an expanded feature matrix, which then trains the XGBoost algorithm to determine whether a patient has heart disease. When assessed alongside hybrid deep learning models combining convolutional neural networks with recurrent neural networks and long short-term memory networks, the ETCXGB approach demonstrated superior diagnostic results. Simulation findings showed a 3.91 percent improvement in diagnostic accuracy over standard methods, outperforming alternative architectures across all measured performance metrics to assist clinical decision-making.
Cardiac disease remains a primary driver of global mortality, making early and reliable identification essential. By integrating two distinct machine learning classifiers into a complementary pipeline, clinical decision support can become significantly more accurate. This approach offers healthcare practitioners more dependable automated tools to identify heart conditions early, which could contribute to improved patient interventions and lower mortality rates.
The technology provides a diagnostic algorithm designed for clinical decision support systems used by medical practitioners and healthcare providers. At present, the evidence rests on simulation outcomes on a cardiac disease dataset, indicating an early to intermediate stage of research. Moving towards practical use would require validation on broader clinical cohorts, testing within operational medical software, and compliance with healthcare regulatory standards.
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This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output. The output of the ETC model was then added to the original feature space by producing an enriched feature matrix, which is then used as input for the XGB model. The new feature matrix is used for training the XGB model, which produces the final result that whether a person has cardiac disease or not, resulting in a high diagnosis accuracy for cardiac disease. In addition to the proposed model, three other hybrid DL models, such as convolutional neural network + recurrent neural network (CNN-RNN), convolutional neural network + long short-term memory (CNN-LSTM), and convolutional neural network + bidirectional long short-term memory (CNN-BLSTM), were also investigated. The proposed ETCXGB model improved the prediction accuracy by 3.91%, while CNN-RNN, CNN-LSTM, and CNN-BLSTM enhanced the prediction accuracy by 1.95%, 2.44%, and 2.45%, respectively, for the diagnosis of cardiac disease. The simulation outcomes illustrate that the proposed ETCXGB hybrid ML outperformed the classical ML and DL models in terms of all performance measures. Therefore, using the proposed hybrid ML model for the diagnosis of cardiac disease will help the medical practitioner make an accurate diagnosis of the disease and will help the healthcare society decrease the mortality rate caused by cardiac disease.
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DOI: 10.3390/bioengineering11121290
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