Prediksi Gagal Jantung Menggunakan Artificial Neural Network

Authors

  • Simeon Yuda Prasetyo Bina Nusantara University

DOI:

https://doi.org/10.33020/saintekom.v13i1.379

Keywords:

heart failure prediction, artificial neural networks, machine learning

Abstract

Cardiovascular disease or heart problems are the leading cause of death worldwide. According to WHO (World Health Organization) every year there are more than 17.9 million deaths worldwide. In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Networks by conducting several hyperparameter tests, namely the number of hidden layers and the number of neuron units in the hidden layer. Based on the test results, the Artificial Neural Network model get the best results by implementing 2 hidden layers with 15 units of neurons in the first hidden layer and 10 units of neurons in the second hidden layer. This model get accuracy on data testing of 92,032% and AUC of 93%.

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References

Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82–93. https://doi.org/10.1016/j.tele.2018.11.007

Anitha, S., & Sridevi, N. (2019). HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES. In Journal of Analysis and Computation. www.ijaconline.com

Gaziano, T. A., Bitton, A., Anand, S., Abrahams-Gessel, S., & Murphy, A. (2010). Growing Epidemic of Coronary Heart Disease in Low- and Middle-Income Countries. Current Problems in Cardiology, 35(2), 72–115. https://doi.org/10.1016/j.cpcardiol.2009.10.002

Kaggle. (n.d.). https://www.kaggle.com/datasets

Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707

Patel, B. C., & Sinha, G. R. (2015). Reliable Computer-aided Diagnosis System using Region based Segmentation of Mammographic Breast Cancer Images. Mathematical Methods and Systems in Science and Engineering, 296.

Powers, D. M. W., & Ailab. (2020). EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION.

Prasetyo, S. Y. (2023). SARS-CoV-2 Detection From Lung CT-scan Images Using Fine Tuning Concept on Deep-CNN Pretrained Model. CESS (Journal of Computing Engineering, System and Science), 8(1), 101–112. www.jurnal.unimed.ac.id

Prasetyo, S. Y., & Nabiilah, G. Z. (2023). PERBANDINGAN MODEL MACHINE LEARNING PADA KLASIFIKASI TUMOR OTAK MENGGUNAKAN FITUR DISCRETE COSINE TRANSFORM. Jurnal Teknologi Terpadu, 9(1). https://journal.nurulfikri.ac.id/index.php/jtt

Prasetyo, S. Y., Nabiilah, G. Z., & Nabila Izdihar, Z. (2022). Age Estimation from Face Image using Discrete Cosine Transform Feature and Artificial Neural Network.

Seckeler, M. D., & Hoke, T. R. (2011). The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease. In Clinical Epidemiology (Vol. 3, Issue 1, pp. 67–84). https://doi.org/10.2147/CLEP.S12977

Sharma, S., & Parmar, M. (2020). Heart Diseases Prediction using Deep Learning Neural Network Model. International Journal of Innovative Technology and Exploring Engineering, 9(3), 2244–2248. https://doi.org/10.35940/ijitee.C9009.019320

Singh, A., & Kumar, R. (2020). Heart Disease Prediction Using Machine Learning Algorithms. International Conference on Electrical and Electronics Engineering, 452–457.

Wahono, R. S., Herman, N. S., & Ahmad, S. (2014). A comparison framework of classification models for software defect prediction. Advanced Science Letters, 20(10–12), 1945–1950. https://doi.org/10.1166/asl.2014.5640

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Published

31-03-2023

How to Cite

Prasetyo, Simeon Yuda. 2023. “Prediksi Gagal Jantung Menggunakan Artificial Neural Network”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 13 (1):79-88. https://doi.org/10.33020/saintekom.v13i1.379.

Issue

Vol. 13 No. 1 (2023): Maret 2023

Section

Articles

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Copyright (c) 2023 Simeon Yuda Prasetyo

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