An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques
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...
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2024-12-01
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| Series: | Bioengineering |
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| author | Hazrat Bilal Yibin Tian Ahmad Ali Yar Muhammad Abid Yahya Basem Abu Izneid Inam Ullah |
| author_facet | Hazrat Bilal Yibin Tian Ahmad Ali Yar Muhammad Abid Yahya Basem Abu Izneid Inam Ullah |
| author_sort | Hazrat Bilal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d10fa9d7879f455e852f921be12753c6 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-d10fa9d7879f455e852f921be12753c62024-12-27T14:11:45ZengMDPI AGBioengineering2306-53542024-12-011112129010.3390/bioengineering11121290An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence TechniquesHazrat Bilal0Yibin Tian1Ahmad Ali2Yar Muhammad3Abid Yahya4Basem Abu Izneid5Inam Ullah6College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518000, ChinaSchool of Computer Science and Engineering, Beihang University, Beijing 100191, ChinaDepartment of Electrical Computer and Telecommunication, Botswana University of Science and Technology Botswana, Plot, Palapye 10071, BotswanaFaculty of Engineering, Department of Robotics and Artificial Intelligence Engineering, Al-Ahliyya Amman University, Amman 19328, JordanDepartment of Computer Engineering, Gachon University, Seongnam 13120, Republic of KoreaThis 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.https://www.mdpi.com/2306-5354/11/12/1290cardiac diseasehybrid machine learning techniqueshybrid deep learning approachesdisease predictioncardiac disease prediction |
| spellingShingle | Hazrat Bilal Yibin Tian Ahmad Ali Yar Muhammad Abid Yahya Basem Abu Izneid Inam Ullah An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques Bioengineering cardiac disease hybrid machine learning techniques hybrid deep learning approaches disease prediction cardiac disease prediction |
| title | An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques |
| title_full | An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques |
| title_fullStr | An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques |
| title_full_unstemmed | An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques |
| title_short | An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques |
| title_sort | intelligent approach for early and accurate predication of cardiac disease using hybrid artificial intelligence techniques |
| topic | cardiac disease hybrid machine learning techniques hybrid deep learning approaches disease prediction cardiac disease prediction |
| url | https://www.mdpi.com/2306-5354/11/12/1290 |
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