Analisis Rekam Medis dengan Metode Data Mining untuk Memprediksi Faktor Risiko Stunting dalam Kesehatan Masyarakat
Abstract
Stunting adalah kondisi serius yang disebabkan oleh malnutrisi berkepanjangan dan infeksi berulang, menghambat pertumbuhan fisik dan perkembangan kognitif anak-anak. Kondisi ini mempengaruhi jutaan anak di seluruh dunia, terutama di negara-negara berkembang, dan memiliki dampak jangka panjang terhadap kesehatan, pendidikan, dan potensi ekonomi mereka. Dalam penelitian ini, data rekam medis terhadap kejadian stunting dianalisis dengan menggunakan 121.000 dataset. Data tersebut diklasifikasikan ke dalam empat status gizi: normal, stunted, tinggi, dan severely stunted, berdasarkan empat variabel utama yaitu usia, jenis kelamin, tinggi badan sebagai label, dan status gizi sebagai variabel target. Model Random forest digunakan untuk menguji keakuratan model dalam memprediksi status gizi anak-anak. Hasil analisis menunjukkan bahwa model ini sangat akurat, dengan nilai rata-rata akurasi sebesar 0,9990 setelah dilakukan 10 kali cross-validation. Temuan ini menunjukkan bahwa Random forest merupakan model yang efektif dalam memprediksi status gizi anak-anak dan dapat digunakan untuk membantu intervensi kesehatan yang lebih tepat sasaran. Penelitian ini juga menyoroti pentingnya penggunaan data rekam medis yang komprehensif dalam pemantauan kesehatan populasi anak-anak
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