ALGORITMA NEURAL NETWORK UNTUK PREDIKSI PENYAKIT JANTUNG
Keywords:
Support Vector machine, Neural Network, Heart disease
Abstract
Heart disease is the occurrence of partial or total blockage of a blood vessel over, as a result of the self blockage deep chemical energy supply to the heart muscle is reduced, resulting in an impaired balance between supply and in predicting heart disease have been carried out by several previous investigators. In this study will be done for heart disease prediction algorithm using a neural network and improved the performance of the neural network algorithm is implemented on the data of heart disease patients. From the test results by measuring method using a neural network based, it is known that neural network algorithms yield accuracy values 91.45%, precision 92.79 %, recall 94.27% and AUC values obtained 0.937. by looking at the accuracy, the algorithm based neural network into the category of groups is very good, because of AUC values between 0.90 – 1.00.
References
Anbarasi, M., Anupriya, E., & Iyengar, N. (2010). Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm. International Journal of Engineering Science and Technology Vol. 2(10), 2010, 5370-53, 5370-5376.
Han, J., & Kember, M. (2006). Data Mining Concepts adn Techniques. San Fransisco: Morgan Kauffman.
Hananta, I. Y., & Muhammad, H. F. (2011). Dietisien Deteksi Dini & Pencegahan 7 Penyakit Penyebab Mati Muda. Yogyakarta: Media Pressindo.
Janosi, A., & Steinbrunn, W. (2011, November 13). UCI MAchine Learning Repository. Retrieved from UCI MAchine Learning Repository: http://archive.ics.uci.edu/ml/datasets/ Heart+Disease
Khemphila, A., & Boonjing, V. (2011). Heart disease Classification using Neural Network and Feature Selection. 2011 21st International Conference on Systems Engineering, 407-409.
Mahmood, A. M., & Kuppa, M. R. (2010). Early Detection Of Clinical Parameters In Heart Desease By Improved Decision Tree Algorithm. 2011 Second Vaagdevi Internasional Conference on Information Technology for Real World Problems, 24-28.
Palaniappan, S., & Awang, R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008 , 343-350. Purnomo, M. H., & Kurniawan, A. (2006). Supervised Neural Network dan Aplikasinya. Yogyakarta: Graha Ilmu.
Santoso, B. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications of Soft Computing. United States of America on: Taylor and Francis Group, LLC.
Subbalakshmi, G., Ramesh, K., & Chinna Rao, M. (2011). Decision Support in Heart Disease Prediction System using Naive Bayes. Indian Journal of Computer Science and Engineering (IJCSE), 170-176.
Widyanto, M. R., & Fatichah, C. (2009). Studi Analisis terhadap metode Support Vector machine dan Boosting Untuk Deteksi Objek Manusia. 161-170.
Xing, Y., Wang, J., Zhao, Z., & Gao, Y. (2007). Combination data mining methods with new medical data to predicting outcome of Coronary Heart Disease. 2007 International Conference on Convergence Information Technology, 868-872.
Han, J., & Kember, M. (2006). Data Mining Concepts adn Techniques. San Fransisco: Morgan Kauffman.
Hananta, I. Y., & Muhammad, H. F. (2011). Dietisien Deteksi Dini & Pencegahan 7 Penyakit Penyebab Mati Muda. Yogyakarta: Media Pressindo.
Janosi, A., & Steinbrunn, W. (2011, November 13). UCI MAchine Learning Repository. Retrieved from UCI MAchine Learning Repository: http://archive.ics.uci.edu/ml/datasets/ Heart+Disease
Khemphila, A., & Boonjing, V. (2011). Heart disease Classification using Neural Network and Feature Selection. 2011 21st International Conference on Systems Engineering, 407-409.
Mahmood, A. M., & Kuppa, M. R. (2010). Early Detection Of Clinical Parameters In Heart Desease By Improved Decision Tree Algorithm. 2011 Second Vaagdevi Internasional Conference on Information Technology for Real World Problems, 24-28.
Palaniappan, S., & Awang, R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008 , 343-350. Purnomo, M. H., & Kurniawan, A. (2006). Supervised Neural Network dan Aplikasinya. Yogyakarta: Graha Ilmu.
Santoso, B. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications of Soft Computing. United States of America on: Taylor and Francis Group, LLC.
Subbalakshmi, G., Ramesh, K., & Chinna Rao, M. (2011). Decision Support in Heart Disease Prediction System using Naive Bayes. Indian Journal of Computer Science and Engineering (IJCSE), 170-176.
Widyanto, M. R., & Fatichah, C. (2009). Studi Analisis terhadap metode Support Vector machine dan Boosting Untuk Deteksi Objek Manusia. 161-170.
Xing, Y., Wang, J., Zhao, Z., & Gao, Y. (2007). Combination data mining methods with new medical data to predicting outcome of Coronary Heart Disease. 2007 International Conference on Convergence Information Technology, 868-872.
Published
2013-03-15
How to Cite
Rifai, B. (2013). ALGORITMA NEURAL NETWORK UNTUK PREDIKSI PENYAKIT JANTUNG. Jurnal Techno Nusa Mandiri, 10(1), 1-9. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/554
Section
Articles
Copyright (c) 2013 Bakhtiar Rifai
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