DETEKSI SUBGRUP NYERI DADA DENGAN UMAP DAN STRATIFIKASI RISIKO
DOI:
https://doi.org/10.23960/jitet.v13i3S1.7480Abstract Views: 38 File Views: 23
Keywords:
UMAP algorithm, K-means clustering, Risk stratification, Chest pain, Machine learningAbstract
Stratifikasi risiko pada pasien dengan nyeri dada merupakan tantangan klinis yang kompleks karena heterogenitas presentasi dan prognosis yang beragam. Penelitian ini bertujuan mengidentifikasi subgrup tersembunyi dalam tipe nyeri dada menggunakan teknik pembelajaran tanpa supervisi untuk meningkatkan akurasi prediksi risiko penyakit jantung. Dataset yang terdiri dari 918 pasien dengan 12 variabel klinis dianalisis menggunakan kombinasi UMAP (Uniform Manifold Approximation and Projection) untuk reduksi dimensi dan K-means clustering untuk identifikasi subgrup. Hasil clustering kemudian diintegrasikan dengan Random Forest classifier untuk prediksi risiko. Analisis berhasil mengidentifikasi 4 cluster dengan karakteristik risiko yang berbeda signifikan. Cluster 1 menunjukkan risiko tertinggi (86,9%) dengan dominasi nyeri dada asimtomatik dan angina akibat olahraga, sedangkan cluster 2 dan 3 memiliki risiko lebih rendah (32,2% dan 22,3%). Model prediksi yang dikembangkan mencapai akurasi 88,0% dengan AUC-ROC 0,935. Pendekatan clustering ini berhasil mengungkap pola tersembunyi yang tidak terdeteksi melalui analisis konvensional, memberikan wawasan baru untuk stratifikasi risiko yang lebih presisi dalam praktik klinis.
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