Optimasi Akurasi Prediksi Penyakit Kanker Payudara Menggunakan Metode Random Forest
DOI:
https://doi.org/10.52436/1.jpti.802Kata Kunci:
Akurasi model, Diagnosis medis, Optimasi prediksi, Kanker payudara, Random forestAbstrak
Kanker payudara merupakan ancaman serius bagi kesehatan wanita, dengan deteksi dini dan diagnosis akurat yang sangat penting untuk meningkatkan hasil pasien dan mengurangi kematian; namun, akurasi dan keandalan metode diagnosis saat ini masih perlu ditingkatkan. Penelitian ini bertujuan untuk mengoptimalkan metode Random Forest dalam prediksi kanker payudara, meningkatkan akurasi dan efisiensi model diagnosa. Menggunakan dataset Kaggle yang mencakup 569 pasien dengan 30 fitur karakteristik sel payudara, penelitian ini melalui proses pemuatan data, pra-pemrosesan (pembersihan dan normalisasi), pembagian data latih dan uji, pelatihan model Random Forest, prediksi, evaluasi model dengan metrik akurasi, presisi, recall, F1-score, dan AUC-ROC, serta visualisasi hasil. Hasilnya, penelitian mencapai akurasi 96%, peningkatan dari 95% yang dicapai sebelumnya, dengan model Random Forest yang dioptimalkan menunjukkan performa sangat baik dalam mengklasifikasikan kanker payudara, memiliki presisi 98%, recall 93%, dan F1-Score 95%, serta kemampuan klasifikasi sempurna dengan AUC 1.00. Model ini dapat menjadi alat yang efektif dalam diagnosis medis kanker payudara, meningkatkan hasil pasien, dan mengurangi kematian, sehingga penelitian ini merekomendasikan penggunaan metode Random Forest yang dioptimalkan sebagai alat prediksi kanker payudara yang efektif dan efisien.
Unduhan
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