Optimasi Prediksi Gagal Jantung dengan Teknik Ensemble Bagging Pada Neural Network - Ariawan, Angga (LPPM STIKOM Tunas Bangsa, 2024)
Prediction of heart failure is an important step in the early management of serious cardiovascular disease. This research uses the Ensemble bagging algorithm with Neural Network. The dataset is taken from Heart Failure Clinical Records available in the UCI Machine Learning Repository. The use of training data in this research was 80% of the total data set, and 20% of the test data. The dataset is divided into two feature models, namely features with categorical data and continuous data. At the data transformation stage, continuous data is subjected to value scaling. several single classifier machine learning algorithms have been tested in this research such as Logistic Regression, Artificial Neural Networks (ANN), Naïve Bayes, SVM. Single classifier Artificial Neural Networks (ANN) produces an accuracy value of 82%. Ensemble learning using the bagging method on Artificial Neural Networks (ANN) was carried out to get a higher accuracy value. Ensemble learning using the bagging method on Artificial Neural Networks (ANN) obtained an accuracy value of 98%. This method is proven to have increased the accuracy value by 16% better than just using a Single Classifier Artificial Neural Networks (ANN) in the case of the Heart Failure Clinical Records dataset.