A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques

Abdeldjouad, Fatma Zahra; Menaouer, Brahami; Matta, Nada

doi:10.1007/978-3-030-51517-1_26

Cited by 50 publications

(34 citation statements)

References 4 publications

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“…The application of artificial intelligence and machine learning algorithms has gained much popularity in recent years due to the improved accuracy and efficiency of making predictions [26]. The importance of research in this area lies in the possibility to develop and select models with the highest accuracy and efficiency [27]. Hybrid models which integrate different machine learning models with information systems (major factors) are a promising approach for disease prediction [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

et al. 2021

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Cardiovascular diseases are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

et al. 2021

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“…When increasing the accuracy of risk and attributes, the author can use the analysis of the K-nearest neighbour algorithm, Naïve Bayes, and neural network. e accuracy is increased with a low number of attributes, which is possible by using various methods [15]. Prerana et al predicted the risk level of probabilistic analysis and classification (PAC) and heart disease completed by machine learning technique.…”

Section: Part 2 Is 'Diastole'mentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

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“…Here, they have restricted to define only three categories; PVC, PAC, and other. Abdeldjouad et al, Vijayashree et al, Marikani et al, Garate et al 5‐8 highlighted about the feature selection methods like Chi‐square selector and principal component analysis (PCA), Wrapper methods, particle swarm optimization (PSO) with SVM Classifier for determining the most significant features for the classification of heart diseases but they failed to use altogether. Magesh et al 9 proposed an optimal feature selection criterion, Cluster‐based DT—RF learning with large data samples and have used entropy methods to achieve the high classification accuracy of 86.70% after applying preprocessing techniques as well as taken all features into consideration.…”

Section: Related Workmentioning

confidence: 99%

“…However, to build more accurate and robust health prediction system, it is more feasible to combine the results from multiple approaches. Abdeldjouad et al 5 have performed analysis of few classification algorithms and proposed a hybrid approach for the diagnosis and prediction of cardio‐vascular diseases. The authors compared the results with two models, Weka and Keel tool in order to select the most prominent classification model.…”

Section: Related Workmentioning

confidence: 99%

Internet of medical things‐based multitiered and hybrid architectural framework for effective heart disease prediction model

Priya

Jinny

2022

Concurrency and Computation

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Cardiovascular and chronic respiratory diseases are becoming a global threat. The alarming fact about Cardio-vascular diseases (CVD) is that rural areas are recording a high number of heart failures compared to urban areas. The proposed research work intends to create an effective medical setup in country-wide through Internet of Medical Things (IoMT)-based multitiered and hybrid architectural framework and Machine Learning (ML) model. The proposed architecture collects the medical data using the IoMT devices. During data preprocessing, categorical data are encoded using one-hot mechanism and is normalized using standard scalar technique. In addition, featureselection mechanisms are applied to choose the best significant features using hybrid sequence of Filter-Wrapper-Ensemble approach. To reduce the diagnostic barriers in health prediction model, an efficient method of Bayesian optimization for hyperparameter tuning and k-fold cross-validation resampling technique is applied to evaluate various conventional and ensemble ML techniques. The prediction results show the high accuracy of 86.47% and 85.81% using Logistic Regression and Extreme Gradient Boosting (XGBoosting) algorithm, respectively. In particular, the accuracy obtained using decision tree for the proposed approach is higher (80.53%) than that of existing approach (73.22%).

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A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques

Cited by 50 publications

References 4 publications

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Machine Learning Technology-Based Heart Disease Detection Models

Internet of medical things‐based multitiered and hybrid architectural framework for effective heart disease prediction model

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