Identification of Cardiovascular Diseases Using Machine Learning

Louridi, Nabaouia; Amar, Meryem; Ouahidi, Bouabid El

doi:10.1109/cmt.2019.8931411

Cited by 47 publications

(24 citation statements)

References 6 publications

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“…The results show that the accuracy of LR reaches 85.86%, which is better than XGBoost, which has an accuracy of 84.46%. Louridi et al [ 5 ] used the UCI machine learning repository and compared three methods: SVM, k-Nearest Neighbor (kNN), and NB. Experiments show that the SVM with linear kernel has the best effect, with an accuracy of 86.8%.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

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Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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“…Setiawan et al [22] implemented ANN with Rough Set Theory (RST) ,(ANN-RST) attribute reduction to predict the real missing attribute values on heart disease data, this method outperforms well in comparison to other techniques such as ANN, Piecewise Linear Network-Orthonormal Least Square feature selection (PLN-OLS), and KNN. Louridi et al [23]. Suggest filling up missing values with the mean value instead of ignoring them, this approach gives better results with SVM in classification (Fig.…”

Section: Related Workmentioning

confidence: 98%

Machine learning-based identification of patients with a cardiovascular defect

2021

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Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models.

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“…N. Louridi et al [30] proposed a solution to identify the presence/absence of heart disease by replacing missing values with the mean values during pre-processing. They trained three machine learning algorithms, namely, NB, SVM (linear and radial basis func-tion), and KNN, by splitting the Cleveland dataset of 303 instances and 13 attributes into 50:50, 70:30, 75:25, and 80:20 training and testing ratios.…”

Section: Related Workmentioning

confidence: 99%

“…From the experimental works, it is understood that data pre-processing and feature selection can substantially enhance the classification accuracy of machine learning algorithms. During pre-processing, most researchers [18,19,21,22,26,[29][30][31][32] replaced the missing values, either by using the mean value or the majority mark of that attribute, to make sure the dataset was comprehensive. In some works [20,24,25,27], the missing valued instances were removed.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

et al. 2021

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Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

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Identification of Cardiovascular Diseases Using Machine Learning

Cited by 47 publications

References 6 publications

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Machine learning-based identification of patients with a cardiovascular defect

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

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