Bilal Hazrat, Tian Yibin, Ali Ahmad, Muhammad Yar, Yahya Abid, Izneid Basem Abu, Ullah Inam
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, China.
Bioengineering (Basel). 2024 Dec 19;11(12):1290. doi: 10.3390/bioengineering11121290.
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.
本研究提出了一种用于心脏病早期准确诊断的新型混合机器学习（ML）模型。所提出的模型是两种强大的集成ML模型的组合，即极端随机树分类器（ETC）和XGBoost（XGB），由此产生了一个名为ETCXGB的混合模型。首先，将所使用的心脏病数据集的所有特征作为输入提供给ETC模型，该模型通过提取预测概率对其进行处理并产生输出。然后，ETC模型的输出通过生成一个丰富的特征矩阵被添加到原始特征空间中，该特征矩阵随后被用作XGB模型的输入。新的特征矩阵用于训练XGB模型，该模型产生一个人是否患有心脏病的最终结果，从而实现对心脏病的高诊断准确率。除了所提出的模型之外，还研究了其他三种混合深度学习（DL）模型，如卷积神经网络+循环神经网络（CNN-RNN）、卷积神经网络+长短期记忆网络（CNN-LSTM）和卷积神经网络+双向长短期记忆网络（CNN-BLSTM）。所提出的ETCXGB模型在心脏病诊断方面将预测准确率提高了3.91%，而CNN-RNN、CNN-LSTM和CNN-BLSTM分别将预测准确率提高了1.95%、2.44%和2.45%。仿真结果表明，所提出的ETCXGB混合ML模型在所有性能指标方面均优于经典的ML和DL模型。因此，使用所提出的混合ML模型进行心脏病诊断将有助于医生对疾病做出准确诊断，并有助于医疗保健行业降低由心脏病导致的死亡率。
