Diagnosis of Heart Disease Using Machine Learning Methods

Abdullah, Azian Azamimi; Alhadi, Nazirah Ahmad; Khairunizam, Wan

doi:10.1007/978-981-16-0866-7_6

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Feature extraction methods using ECG signals, such as discrete wavelet transform (DWT) and SVM classifiers, are applicable in early heart disease detection [10]. Machine learning algorithms such as Random Forest (RF), SVM, LR, and KNN have been used with feature selection methods to diagnose heart disease, with RF and RFFS yielding the highest accuracy [11]. Data mining methods, including decision trees, KNN classifiers, naive Bayes, random forests, and SVM, have also been used for heart disease diagnosis, with SVM showing the best performance [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

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Section: Introductionmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Heart disease is one of the most complicated diseases, and it affects a large number of individuals throughout the world. In healthcare, particularly cardiology, early and accurate detection of cardiac disease is critical. The Heart Disease Data Set-UCI repository collects data on heart disease. The search space and complexity of the classification models are increased by this raw dataset, which contains redundant and inconsistent data. We need to eliminate the redundant and unnecessary elements from the data to improve classification accuracy. As a consequence, feature selection approaches might be useful for reducing the cost of diagnosis by identifying the most important qualities. This research developed an ensemble classification model based on a feature selection approach in which selected features play a role in classification. Accordingly, a classification approach was introduced using ensemble learning with a genetic algorithm, feature selection, and biomedical test values to diagnose heart disease. Based on the results, it is deduced that the benefits of using the feature selection method vary depending on the utilized machine learning technique. However, the best-proposed model based on the combination of genetic algorithm and the ensemble learning model has achieved an accuracy of 97.57% on the considered datasets. The suggested diagnosis system achieved better accuracy than previously proposed methods and can easily be implemented in healthcare to identify heart disease. Graphical abstract

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Diagnosis of Heart Disease Using Machine Learning Methods

Cited by 2 publications

References 12 publications

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

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