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Abstract

Based on data from the World Health Organization (WHO), one type of heart disease namely coronary heart disease is the deadliest disease in the world. In 2016 at least 9,4 million people died caused by coronary heart disease. In Indonesia, deaths caused by heart disease, blood vessel (CVD), and respiratory disorders are the fourth highest in ASEAN (23,1%). Because of the danger of coronary heart disease, we need a system or model that can predict heart disease early, so that it can be treated early and can reduce the death rate caused by heart disease. This study uses principal component analysis (PCA) to make a linear combination of variables that have a high correlation so that the assumption of multicollinearity in the data can be resolved. For the prediction, this study uses binary logistic regression to predict heart disease based on existing factors. The result of the PCA there is 7 component variables with a total variance that can be explained as much as 72,9%. From the Bartlett test of the PCA data, the obtained p-value is 1 which means that there is no multicollinearity in the data. Predictive analysis using binary logistic regression based on PCA’s data was proven to increase the accuracy to 85%.

Keywords

Coronary Heart Multicollinearity Principal Component Analysis Binary Logistic Regression

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Author Biographies

M Fauzan Azhari, Universitas Islam Indonesia, Indonesia

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Farah Ayu Fitriani, Universitas Islam Indonesia, Indonesia

 

 

How to Cite
Azhari, M. F., & Fitriani, F. A. (2022). Coronary Heart Disease Risk Prediction Using Binary Logistic Regression Based on Principal Component Analysis. Enthusiastic : International Journal of Applied Statistics and Data Science, 2(1), 47–55. https://doi.org/10.20885/enthusiastic.vol2.iss1.art6
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References

  1. D. Maulana and R. Yahya, "Implementasi Algoritma Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung di Indonesia Menggunakan Rapid Miner," SIGMA Information Technology Journal, 2019.
  2. B. M. Metisen dan H. L. Sari, “Analisis Clustering Menggunakan Metode K-Means dalam Mengelompokan Penjualan Produk pada Swayalan Fadhila,” 2015.
  3. J. Fitriany and I. Annisa, "Demam Reumatik Akut," Jurnal Averrous, vol. 5, p. 2, 2019.
  4. "World Health Organization," 2018. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
  5. D. Derisma, "Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining," Journal of Applied Informatics and Computing, 2020.
  6. E. Supriyadi, S. Mariani and Sugiman. , "Perbandingan Metode Partial Least Square (PLS) dan Principal Component Regression (PCR) untuk Mengatasi Multikolinearitas pada Model Regresi Linear Berganda," UNNES Journal of Mathematics, 2017.
  7. N. R. Sari and W. f. Mahmudy, "Fuzzy Inference System Tsukamoto untuk Menentukan Kelayakan Calon Pegawai," in Seminar Nasional Sistem Informasi Indonesia 2015, 2015.
  8. P. R, A. A and R. A.A., "Analisis Ekstraksi Fitur Principal Component Analysis pada Klasifikasi Microarray dan Menggunakan Classification and Regression Trees," eProceedings of Engineering, 2019.
  9. D. T. C. Sirait, A. and W. Astuti, "Analisis Perbandingan Reduksi Dimensi Principal Component Analysis (PCA) dan," e-Proceeding of Engineering : Vol.6, 2019.
  10. O. Haloho, P. Sembiring and A. Manurung, "Penerapan Analisis Regresilogistik pada Pemakaian Alat Kontrasepsiwanita," Saintia Matematika, vol. 1, p. 53, 2013.
  11. A. Alahmad, A. I. S. Azis, B. Santoso and S. Taliki, "Prediksi Penyakit Jantung Menggunakan Metode-Metode Machine Learning Berbasis Ensemble – Weighted Vote," JEPIN (Jurnal dukasi dan Penelitian Informatika), 2019.
  12. M. Khairani, A. Susanta and N. A. Y. B, "Analisis Tingkat Kognitif Soal Modul Pengayaan Kelas VIII Materi Persamaan Garis Lurus dan Sistem Persamaan Linear Dua Variabel Berdasarkan Taksonomi Bloom Revisi," Jurnal Edukasi Matematika dan Sains, 2021.
  13. F. Yuliani, F. Oenzil and D. Iryani, "Hubungan Berbagai Faktor Risiko Terhadap Kejadian Penyakit Jantung Koroner Pada Penderita Diabetes Melitus Tipe 2," Jurnal Kesehatan Andalas, vol. 3, 2014.
  14. D. Zahrawardani, K. S. Herlambang and H. D. Anggraheny, "Analisis Faktor Risiko Kejadian Penyakit Jantung Koroner di RSUP Dr Kariadi Semarang," Jurnal Kedokteran Muhammadiyah, vol. 1, 2012.
  15. D. L. Mihardja and H. Siswoyo, "Prevalensi dan Faktor Determinan Penyakit Jantung di Indonesia," Buletin Penelitian Kesehatan, vol. 44, 2016.