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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Anahar, Muhammad Asyraf Bin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Co-Authors Addini, Ezza Al-Sawaff, Zaid Husham Arrazak, Fadlan Manickam, Selvakumar Melyani, Nasya Amirah Sari, Windy Junita