Authors:
Verdi Yasin
1
;
Selli Oktaviani
2
;
Muryan Awaludin
3
and
Ifan Junaedi
1
Affiliations:
1
Faculty of Computer Science, STMIK Jayakarta, Jakarta, Indonesia
;
2
Engineering Study Program, STIKOM Tunas Bangsa, Pemtangsiantar, Indonesia
;
3
Faculty of Industrial Technology, Universitas Dirgantara Marsekal Suryadarma, Jakarta, Indonesia
Keyword(s):
Levenberg Marquardt Backpropagation Algorithm, Predicting Potential Mortality, Heart Failure.
Abstract:
Heart failure is one of the most common disorders that attack the heart and blood vessels throughout the world, resulting in a high average population death rate, and illness also has an impact financially, especially for the elderly. This study focuses on predicting the potential for death in heart failure using the Levenberg Marquart algorithm. The data for predicting the potential for death in heart failure was taken from Kaggle, which consisted of 299 records. Attributes used to predict potential death in heart failure consist of 11 attributes, namely age, gender, anaemia, creatinine phosphokinase, diabetes, ejection fraction, high blood pressure, platelets, serum creatinine, serum sodium, smoking, and death events. The results of this study are predictions of potential death in heart failure with MSE training and testing = 0.0150. With 11-7-1 architecture.