Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome
Dublin Core
Title
Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome
Subject
artificial intelligence, data processing, machine learning, random forest algorithm,supervised learning
Description
This paper explains the use of the Random Forest Algorithm to investigate theCase of Acute Coronary Syndrome (ACS). Theobjectives of this study are to review the evaluation of the use of data sciencetechniques and machine learning algorithms in creating a model that can classify whether or not cases of acute coronary syndrome occur.The research method used in this study refers to the IBM Foundational Methodology for Data Science, include: i) inventorying dataset about ACS, ii) preprocessing for the data into four sub-processes, i.e. requirements, collection, understanding, and preparation, iii) determination of RFA, i.e. the "n" of the tree which will form a forest and forming trees from the random forest that has been created, and iv) determination of the model evaluation and result in analysis based on Python programming language. Based on the experiments that the learning have been conducted using a random forest machine-learning algorithm with an n-estimator value of 100 and each tree's depth (max depth) with a value of 4, learning scenarios of 70:30, 80:20, and 90:10 on 444 cases of acute coronary syndrome data. The results show that the 70:30 scenario model has the best results, with an accuracy value of 83.45%, a precision value of 85%, and a recall value of 92.4%. Conclusions obtained from the experiment results were evaluated with various statistical metrics (accuracy, precision, and recall) in each learning scenario on 444 cases of acute coronary syndrome data with a cross-validation value of 10 fold.
Creator
Eka Pandu Cynthia1, M. Afif Rizky A.2, Alwis Nazir3, Fadhilah Syafria4
Source
https://jurnal.iaii.or.id/index.php/RESTI/issue/view/22
Publisher
UIN Sultan Syarif Kasim Riau
Date
30 april 2021
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Eka Pandu Cynthia1, M. Afif Rizky A.2, Alwis Nazir3, Fadhilah Syafria4, “Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome,” Repository Horizon University Indonesia, accessed June 8, 2025, https://repository.horizon.ac.id/items/show/8589.