Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung

Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung

Authors

  • Agustiena Merdekawati a:1:{s:5:"en_US";s:35:"Universitas Bina Sarana Informatika";}

DOI:

https://doi.org/10.22303/csrid.14.3.2022.188-202

Keywords:

Comparison, Algorithm C4.5, Algorithm C4.5 with PSO, Life Expectancy, Heart Failure

Abstract

The heart is the most vital organ of the body. Heart failure is the leading cause of death with the largest number of cases. Therefore, it is necessary to estimate the biggest factor in life expectancy in patients with heart failure, so as to reduce mortality. In predicting the life expectancy of heart failure by using Knowledge Discovery in Database (KDD) it is possible to find predictive patterns of life expectancy for heart failure, so that it can reduce mortality. In this study using the C4.5 algorithm and the C4.5 algorithm with PSO (Particle Swarm Optimization) to obtain a predictive pattern of life expectancy for heart failure which then obtained the percentage of precision, recall and accuracy. This research is to produce a predictive pattern of life expectancy for heart failure with the criteria for the length of time the action has a top priority. By using the C4.5 algorithm, an accuracy of 73.33% is obtained, while using the C4.5 and PSO algorithms an accuracy of 99.00% is obtained, so it can be concluded based on the accuracy level that the C4.5 and PSO algorithm modeling has a higher accuracy than the C4.5 algorithm. . By using the C4.5 algorithm, the ROC graph accuracy is 0.897%, while using the C4.5 and PSO algorithms the ROC graph accuracy is 1.00%, so it can be concluded based on the ROC graph accuracy level that the C4.5 and PSO algorithm modeling has more accuracy. higher than the C4.5 algorithm.

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Published

2022-12-20

How to Cite

Merdekawati, A. (2022). Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung: Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung. Computer Science Research and Its Development Journal, 14(3), 188–202. https://doi.org/10.22303/csrid.14.3.2022.188-202

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Section

Articles