Aplikasi Deteksi Dini Penyakit Stroke Menggunakan Streamlit
DOI:
https://doi.org/10.29407/xgvr9h34Abstract
Di seluruh dunia, stroke adalah penyebab kematian dan kecacatan jangka panjang nomor dua. Untuk mengurangi risiko komplikasi dan meningkatkan peluang kesembuhan pasien, deteksi dini stroke sangat penting. Tujuan penelitian ini adalah untuk membuat aplikasi deteksi dini stroke berbasis pembelajaran mesin yang lebih akurat dengan menggunakan algoritma Random Forest dan Adaptive Boosting (AdaBoost). Dataset Kaggle dengan 5.110 sampel dan dua belas fitur digunakan. Implementasi model, pembagian dataset (80 persen pelatihan, 20 persen pengujian), dan pra-pemrosesan data (menangani nilai yang tidak ada, transformasi data, dan penyeimbangan kelas menggunakan SMOTETomek) adalah tahapan penelitian. Hasilnya menunjukkan akurasi sebesar 96% untuk kedua kelas—stroke dan non-stroke—dengan presisi dan recall yang seimbang. Untuk memfasilitasi visualisasi dan prediksi secara real-time, framework Streamlit digunakan dalam pengembangan aplikasi. Hasil penelitian menunjukkan bahwa kombinasi AdaBoost-Random Forest berhasil dalam klasifikasi stroke. Aplikasi berbasis web ini mungkin menjadi alat bantu yang bermanfaat untuk diagnosis stroke cepat.
Keywords:
AdaBoost, Deteksi Dini, Random Forest, Streamlit##plugins.themes.default.displayStats.downloads##
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