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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Irsyad, As’Ary Sahlul
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Decision Sciences, Operations Research & Management

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MODELING HEART DISEASE CLASSIFICATION USING ROUGH NEURAL NETWORK: A DATA-DRIVEN APPROACH TO THE CLEVELAND HEART DISEASE DATASET Nursyahrina, Nursyahrina; Sahri, Alfi; Irsyad, As’Ary Sahlul; Hafizhah, Nadia Aini
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4848

Abstract

This study implements a Rough Neural Network (RNN) intelligent system method, merging rough sets with neural networks to diagnose heart disease effectively. Using the Cleveland Heart Disease Dataset, rough sets identified nine relevant features for model training, simplifying data complexity. Comparative assessment against traditional neural networks revealed the RNN model's superior performance, achieving 88.52% accuracy, 88.14% F1 score, and 88.85% AUC. This hybrid approach improves predictive accuracy while enhancing efficiency and interpretability. The findings contribute to advancing intelligent systems for heart disease diagnosis, facilitating early detection, and improving patient outcomes. Future research may explore selected features' clinical significance and RNN applicability in different contexts. Keywords: Heart Disease Detection, Rough Neural Network, Rough Set Theory, Neural Networks, Hybrid Intelligent System
Co-Authors Hafizhah, Nadia Aini Nursyahrina, Nursyahrina Sahri, Alfi