DIABETES PREDICTION USING MACHINE LEARNING

Elisabet da Conceicao Pereira, Widyastuti Andriyani

Abstract


Diabetes mellitus merupakan salah satu penyakit metabolik kronis yang prevalensinya terus meningkat secara global. Penyakit ini ditandai oleh tingginya kadar glukosa darah yang dapat menyebabkan. Penelitian ini bertujuan untuk membangun model prediksi diabetes dengan menggunakan algoritma Random Forest berdasarkan data klinis lokal. Metodologi yang digunakan mencakup tahapan pra-pemrosesan data, pelatihan model, dan evaluasi performa menggunakan metrik akurasi, presisi, recall, dan F1-score. Dataset yang digunakan terdiri dari berbagai fitur klinis seperti usia, kadar glukosa, tekanan darah, indeks massa tubuh (BMI), serta riwayat diabetes keluarga. Setelah melalui proses pembersihan data dan normalisasi, model dilatih menggunakan 80% data dan diuji dengan 20% sisanya. Hasil pengujian menunjukkan bahwa algoritma Random Forest memberikan performa yang sangat baik, dengan akurasi mencapai 87%, presisi 84%, recall 82%, dan F1-score sebesar 83%. Hasil ini menunjukkan bahwa algoritma ini mampu memprediksi risiko diabetes dengan ketepatan yang tinggi. Model prediktif ini diharapkan dapat diimplementasikan sebagai sistem pendukung keputusan medis yang membantu tenaga kesehatan dalam melakukan skrining awal terhadap pasien.

Keywords


Diabetes, Machine Learning, Prediksi, Klasifikasi, Diagnosis

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DOI: http://dx.doi.org/10.26798/jiko.v9i3.2104

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