Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms

El-Shafiey, Mohamed G.; Hagag, Ahmed; El‐Dahshan, El‐Sayed A.; Ismail, Mahamod

doi:10.1109/iceem52022.2021.9480625

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…The accuracy of the GCSA model was 95.34% for extracted features and 88.78% for original features. El-Shafiey et al [23] introduced "hybrid classifiers using the ensembled model with majority voting" technique to boost prediction for cardiovascular disease. The dataset was acquired from UCI.…”

Section: Review Of Literaturementioning

confidence: 99%

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A genetic algorithm-based feature selection approach for diabetes prediction

Kangra,

Singh

2024

IJ-AI

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<p>Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges worldwide. For the experiment, two datasets related to diabetes were downloaded from Kaggle and the results of both (datasets) with and without feature selection using the genetic algorithm were compared. Machine learning classifiers and genetic algorithms were combined to increase the precision of diabetes risk prediction. In the preprocessing phase, feature selection, machine learning classifiers, and performance metrics methods were applied to make this study feasible. The results of the experiment showed that genetic algorithm + logistic regression i.e., 80% (accuracy) works better for PIMA diabetes, and for Germany diabetes dataset genetic algorithm + random forest and genetic algorithm + K-Nearest Neighbor i.e., 98.5% performed better than other chosen classifiers. The researchers can better comprehend the importance of feature selection in healthcare through this study.</p>

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Section: Review Of Literaturementioning

confidence: 99%

“…[13]- [15], [19], [22], [24] 6 LR [15], [22], [26] 3 NB [14], [17], [26] 3 RF [13], [14], [16], [17], [18], [22], [23], [26], [27] 9 ANN…”

Section: Review Of Literaturementioning

confidence: 99%

A genetic algorithm-based feature selection approach for diabetes prediction

Kangra,

Singh

2024

IJ-AI

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“…Random Forest Classifier is advantageous as an ensemble method in heart disease detection because it can handle continuous and categorical data, model nonlinear relationships, and adapt hyperparameters for improved performance [39]. Combining multiple decision trees in the Random Forest Classifier improves accuracy by creating a diverse set of decision trees and simultaneously determining the optimal number of trees [40]. This approach generates different training sets with other samples and features to train each tree, improving the performance of random forests and increasing prediction accuracy [41].…”

Section: Literature Reviewmentioning

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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Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms

Cited by 2 publications

References 27 publications

A genetic algorithm-based feature selection approach for diabetes prediction

A genetic algorithm-based feature selection approach for diabetes prediction

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

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