Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning

Cyriac, Sibu; Sivakumar, R.; Raju, Nidhin; Kim, Yong Woon

doi:10.1109/ictc55196.2022.9952655

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Another study used stochastic gradient descent classifiers, LR, and SVM to develop a model with an accuracy of 93% using multiple datasets [34]. For further improving accuracy, Cyriac et al [35] utilized seven different machine-learning models as well as two ensemble methods (soft voting and hard voting). With this approach, the highest accuracy score was achieved at 94.2%.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Chandrasekhar

Peddakrishna

2023

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In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-validation for hyperparameter optimization, determining the best parameters for the model, and assessing performance using accuracy and negative log loss metrics. This study also examined accuracy loss for each fold to evaluate the model’s performance on both benchmark datasets. The soft voting ensemble classifier approach improved accuracies on both datasets and, when compared to existing heart disease prediction studies, this method notably exceeded their results.

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Section: Literature Reviewmentioning

confidence: 99%