OPTIMIZATION OF SUPPORT VECTOR MACHINE WITH SMOTE AND BAYESIAN METHOD FOR HEART FAILURE CLASSIFICATION

Doni Agung Prasetyo; Harminto Mulyo; Nadia Annisa Maori

doi:10.33330/jurteksi.v11i3.4057

Doni Agung Prasetyo Universitas Islam Nahlatul Ulama Jepara
Harminto Mulyo Universitas Islam Nahdlatul Ulama Jepara
Nadia Annisa Maori Universitas Islam Nahdlatul Ulama Jepara

DOI: https://doi.org/10.33330/jurteksi.v11i3.4057

Keywords: bayesian optimization, heart failure, machine learning, SMOTE, SVM

Abstract

Abstract: This study applies an integrated approach to optimize heart failure classification. The main objective is to address the challenge of class imbalance in medical datasets and to improve the accuracy, sensitivity, and generalization of the classification model. The urgency of this issue is emphasized by statistics showing that cardiovascular diseases cause approximately 17.9 million deaths worldwide each year. Using a quantitative experimental approach, this study analyzes the "Heart Failure Prediction Dataset" from Kaggle, which consists of 918 records. The data were processed through normalization and encoding, followed by the application of SMOTE on the training set to balance class distribution. This step successfully increased model accuracy from 88.41% to 90.22% and minority class recall from 0.82 to 0.88. Furthermore, Bayesian Optimization was employed to refine the hyperparameters of SVM, resulting in a final model with an accuracy of 89.13% that demonstrated better generalization. This integrated approach significantly enhances the stability, sensitivity, and generalization of the model, making it a reliable tool for clinical decision support systems in predicting heart failure.

Keywords: bayesian optimization; heart failure; machine learning; SMOTE; SVM.

Abstrak: Penelitian ini menerapkan pendekatan terintegrasi untuk mengoptimalkan klasifikasi gagal jantung. Tujuan utama studi ini adalah untuk mengatasi tantangan ketidakseimbangan kelas dalam dataset medis dan meningkatkan akurasi, sensitivitas, serta generalisasi model klasifikasi. Urgensi ini ditegaskan oleh statistik yang menunjukkan bahwa penyakit kardiovaskular menyebabkan sekitar 17,9 juta kematian setiap tahun secara global. Menggunakan pendekatan eksperimental kuantitatif, penelitian ini menganalisis "Heart Failure Prediction Dataset" dari Kaggle, yang terdiri dari 918 catatan. Data diproses dengan normalisasi dan encoding, lalu SMOTE diterapkan pada data pelatihan untuk menyeimbangkan distribusi kelas. Langkah ini berhasil meningkatkan akurasi dari 88,41% menjadi 90,22% dan recall kelas minoritas dari 0,82 menjadi 0,88. Selanjutnya, Bayesian Optimization menyempurnakan hyperparameter SVM, menghasilkan model akhir dengan akurasi 89,13% yang menunjukkan generalisasi lebih baik. Pendekatan terintegrasi ini secara signifikan meningkatkan stabilitas, sensitivitas, dan generalisasi model. Hasil penelitian ini menjadikannya alat yang andal untuk sistem pendukung keputusan klinis dalam prediksi gagal jantung.

Kata kunci: bayesian optimization; gagal jantung; machine learning; SMOTE; SVM

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