Prediksi Penyakit Jantung Dengan Algoritma Regresi Linier
- Agung Wijayadhi Universitas Peita Bangsa, Indonesia
- Muhammad Makmun Effendi Pelita Bangsa, Bekasi, Indonesia
-
Sugeng Budi Rahardjo
*
Pelita Bangsa, Bekasi,
Indonesia
Abstract
In this study, we evaluated the ability of a linear regression algorithm to predict heart disease risk in individuals. We use data from trusted sources and perform the necessary preprocessing to clean and provide the data for the model. The results of the analysis show that the linear regression algorithm can be used well to predict the risk of heart disease in individuals with a fairly high degree of accuracy. We also evaluated several factors that influence heart disease risk and demonstrated that they could be identified and integrated into our model to improve its performance. In addition, we also evaluated the validation methods used to evaluate our models and demonstrated that they can be used to objectively determine model performance. The results from this study provide a solid foundation for developing a better heart disease prediction system in the future. And the results of this study are quite accurate enough to give good results with a Root Mean Squared Error: 0.379 +/- 0.000 and Squared Error: 0.144 +/- 0.229
References
R. Annisa, “ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI PENDERITA PENYAKIT JANTUNG,” Jurnal Teknik Informatika Kaputama (JTIK), vol. 3, no. 1, 2019.
Z. Nabila, A. Rahman Isnain, and Z. Abidin, “ANALISIS DATA MINING UNTUK CLUSTERING KASUS COVID-19 DI PROVINSI LAMPUNG DENGAN ALGORITMA K-MEANS,” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
“Implementasi Data Mining Untuk Pengelempokan Buku Di Perpustakaan Yayasan Nurul Islam Indonesia Baru Dengan Metode K-Means Clustering Haryani * , Dicky Nofriansyah ** , Ita Mariami ** * Program Studi Sistem Informasi, STMIK Triguna Dharma ** Program Studi Sistem Informasi, STMIK Triguna Dharma,” 2021, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jct/index
“204-Article Text-389-1-10-20130819”.
M. Pengetahuan and B. Data, “PENGANTAR DATA MINING.”
D. B. Srisulistiowati1, M. Khaerudin2, S. Rejeki3, and U. Bhayangkara Jakarta, “SISTEM INFORMASI PREDIKSI PENJUALAN ALAT TULIS KANTOR DENGAN METODE FP-GROWTH (STUDI KASUS TOKO KOPERASI SEKOLAH BINA MULIA).”
P. Sari Ramadhan and N. Safitri STMIK Triguna Dharma, “Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang,” vol. 18, no. SAINTIKOM, pp. 55–61, 2019, [Online]. Available: https://sirusa.bps.go.id/index.php
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