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All Journal Jurnal Keperawatan Jurnal Manajemen Pendidikan: Jurnal Ilmiah Administrasi, Manajemen dan Kepemimpinan Pendidikan TRANSIENT: Jurnal Ilmiah Teknik Elektro Buletin Sumber Daya Geologi ILKOM Jurnal Ilmiah JISIP: Jurnal Ilmu Sosial dan Pendidikan Jurnal Ilmiah Rekayasa Sipil Khazanah Intelektual These proceedings represent the work of researchers participating in The International Conference on Elementary Education (ICEE) which is being hosted by the Elementary Education Study Programme School of Postgraduate Studies, Universitas Pendidikan Khatulistiwa Nursing Journal Bima Abdi: Jurnal Pengabdian Masyarakat Bestari: Jurnal Studi Pendidikan Islam Alifbata: Jurnal Pendidikan Dasar SocServe: Jurnal Pengabdian kepada Masyarakat Jurnal Meteorologi dan Geofisika Jurnal Maisyatuna
Arif Munandar
STMIK Amikom Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients Munandar, Arif; Maulana Baihaqi, Wiga; Nurhopipah, Ade
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1632.344-352

Abstract

Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.
Co-Authors Achmad Hidayatno Amalia, Afrida Dika Arsa Arsa Azizah, Rahma Nurul Budiwibowo, Arif Candra Darmawan, Candra Dwiatmojo, Nia Firdianti Fajlurrahman, Imam Fithriana, Dina Herli Salim Hidayanti, Arifatul Husna, Difa’ul M Tahir Maulana Baihaqi, Wiga Meinofelia, Erika Muhammad Subhan Muslimah Muslimah Ni Nyoman Santi Tri Ulandari, Ni Nyoman Santi Nurhopipah, Ade Permana, Lano Adhitya Permana, Lano Adhitya Rahayu, Ida Rahmadhani, Sanyyah Putri Ridwan, Ridwan Risdianto, Dikdik Risdianto, Dikdik Setiawan, Dede Iim Setiawan, Dede Iim Shanti Wardaningsih Sri Utaminingsih Su'ad, Su'ad Suhendrik Hanwar Sunarsi, Denok Supriyatna, Encep Teguh Prakoso Tulizza, Dike Rina Wahyu Hardyanto Wijaya, Alwan Wiujianna, Atri Wulandari, Mareta