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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Kesehatan Hesti Wira Sakti Journal of Information Technology and Computer Science TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat The Journal of Enhanced Studies in Informatics and Computer Applications Journal of Informatics and Advanced Computing
Nindynar Rikatsih
Science and Health of Hospital dr. Soepraoen
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Mathematics Medicine & Pharmacology Nursing Public Health

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PREDIKSI PASIEN DENGAN PENYAKIT KARDIOVASKULAR MENGGUNAKAN RANDOM FOREST Mochammad Anshori; Nindynar Rikatsih; M. Syauqi Haris
TEKTRIKA Vol 7 No 2 (2022): TEKTRIKA Vol.7 No.2 2022
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v7i2.5279

Abstract

Cardiovascular disease is one of the deadliest diseases in the world. This is evidenced by data released by WHO which shows around 18 million deaths. This disease causes the cessation of the heartbeat which is the main source of life for the human body.This disease is caused by various things including an unhealthy lifestyle. Examples are consuming cigarettes and alcohol. In addition, it is also caused by other factors, namely health problems such as high blood pressure, cholesterol, diabetes, depression, or anxiety. The cardiovascular disease tends to be difficult to cure, therefore a precise and accurate prediction is needed in diagnosing patients. One method of making predictions is using machine learning techniques. In machine learning, there are various methods that can be used, one of which is the decision tree-based method, namely random forest. Before the random forest is implemented to create a model, the data is pre-processed by normalizing and applying cross-validation with k-fold = 10. The prediction results with the random forest in this study provide an accuracy of 98%. This accuracy is higher when compared to previous studies with the same dataset, namely 96.75% using the ensemble method and 91.61% with logistic regression. On this basis, it proves that the random forest can be used to predict cardiovascular disease. Key Words: cardiovascular disease, tree model, random forest, machine learning.
Co-Authors Ahsanun Naseh Khudori M. Syauqi Haris Makhrus, Moh Ali Makhrus, Moh. Ali Mochamad Anshori Prasetiyo, Bagus Dadang Rika, Faurika Risqy Siwi Pradini Salsabila, Dhea Putri Silvia Intan Wardani Syafrial Syafrial Syafrial Syafrial Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy