Optimasi Prediksi Gagal Jantung dengan Teknik Ensemble Bagging Pada Neural Network

Angga Ariawan(1*),

(1) Universitas Media Nusantara Citra, Jakarta Barat, Indonesia
(*) Corresponding Author

Abstract


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.

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References


L. Ghani Et Al., ‘Faktor Risiko Dominan Penyakit Jantung Koroner Di Indonesia Dominant Risk Factors Of Coronary Heart Disease In Indonesia’.

A. Dwi Erawati, ‘Peningkatan Pengetahuan Tentang Penyakit Jantung

Koroner’, Jurnal Abdimas-Hip, Vol. 2, 2021.

Mamat Supriyono, ‘Tesis Faktor-Faktor Risiko Yang Berpengaruh Terhadap Kejadian Penyakit Jantung Koroner Pada Kelompok Usia < 45 Tahun Program Pasca Sarjana-Magister Epidemiologi Universitas Diponegoro Semarang Tahun 2008’.

D. Das, Md. I. Abu Bakar, Rajshahi University. Faculty Of Engineering, Institute Of Electrical And Electronics Engineers. Bangladesh Section., And Institute Of Electrical And Electronics Engineers, 5th International Conference On Computer, Communication, Chemical, Materials And Electronic Engineering : Ic4me2 : 11-12 July, 2019.

A. Mehmood Et Al., ‘Prediction Of Heart Disease Using Deep Convolutional Neural Networks’, Arab J Sci Eng, Vol. 46, No. 4, Pp. 3409–3422, Apr. 2021, Doi: 10.1007/S13369-020-05105-1.

M. Ward, K. Malmsten, H. Salamy, And C. H. Min, ‘Data Balanced Bagging Ensemble Of Convolutional-Lstm Neural Networks For Time Series Data Classification With An Imbalanced Dataset’, In Proceedings - Ieee International Symposium On Circuits And Systems, Institute Of Electrical And Electronics Engineers Inc., 2021. Doi: 10.1109/Iscas51556.2021.9401389.

Dongbei Gong Xue Yuan, Ieee Control Systems Society, And Ieee Singapore Section. Industrial Electronics Chapter, The 26th Chinese Control And Decision Conference (2014 Ccdc) : 31 May - 2 June 2014, Changsha, China.

D. Pebrianti, ‘Technical Job Distribution At Bsd Sharp Service Center Using Combination Of Naïve Bayes And K-Nearest Neighbour’.




DOI: https://doi.org/10.30645/kesatria.v5i3.426

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.426.g422

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