Random Forest Algorithm to Investigate the Case of Acute Coronary Syndrome

• Published in: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (Online) Vol. 5; no. 2; pp. 369 - 378
• Main Authors: Cynthia, Eka Pandu, M. Afif Rizky A., Nazir, Alwis, Syafria, Fadhilah
• Format: Journal Article
• Language: English
• Published: Ikatan Ahli Informatika Indonesia 29.04.2021
• Subjects: artificial intelligence, data processing, machine learning, random forest algorithm, supervised learning
• ISSN: 2580-0760; 2580-0760
• DOI: 10.29207/resti.v5i2.3000

This paper explains the use of the Random Forest Algorithm to investigate the Case of Acute Coronary Syndrome (ACS). The objectives of this study are to review the evaluation of the use of data science techniques 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.