Analisis untuk memprediksi diabetes menggunakan data mining
Abstrak
Abstrak - Penambangan data sangat penting untuk mengekstraksi pola dan wawasan berharga dari kumpulan data yang luas, memanfaatkan kecerdasan buatan dan teknik analisis data tingkat lanjut di berbagai domain. Diabetes, kelainan metabolisme yang ditandai dengan peningkatan kadar glukosa darah, menimbulkan risiko kesehatan yang signifikan, termasuk komplikasi kardiovaskular dan ginjal jika tidak diobati. Penambangan data memainkan peran penting dalam mengeksplorasi dan memprediksi diabetes dengan mengidentifikasi populasi berisiko tinggi, sehingga memungkinkan strategi intervensi dini seperti perubahan gaya hidup dan permulaan pengobatan tepat waktu.
Menganalisis kumpulan data komprehensif yang mencakup faktor-faktor terkait diabetes seperti berat badan, tekanan darah, kadar glukosa darah, dan kecenderungan genetik, penambangan data membangun model prediktif untuk menilai risiko dan menerapkan intervensi yang ditargetkan. Dalam studi komprehensif yang melibatkan 768 kasus (268 positif dan 500 negatif), Regresi Logistik mencapai akurasi 70%, dengan recall 57% dan skor F1 0,63, Naive Bayes (GaussianNB) mencapai akurasi 68%, dengan rasio recall 54% dan skor F1 0,61, Decision Tree Classifier mencapai akurasi 66%, dengan rasio recall 62% dan skor F1 0,64, Random Forest mencapai akurasi 70%, dengan rasio recall 59% dan skor F1 0,64, XGBClassifier mencapai akurasi 66%, dengan rasio recall 58% dan skor F1 0,62.
Analisis ini menggarisbawahi adanya trade-off antara presisi dan recall, khususnya dalam mengklasifikasikan kasus diabetes berisiko tinggi. Presisi tinggi mengurangi positif palsu tetapi dapat menurunkan recall, sehingga berpotensi melewatkan kasus positif benar. Sebaliknya, penekanan pada ingatan dapat meningkatkan hasil positif palsu. Mencapai keseimbangan antara metrik ini sangat penting untuk prediksi diabetes yang efektif dan strategi perawatan kesehatan yang disesuaikan. Abstrak ini merangkum peran penting penggalian data dalam penelitian diabetes, menekankan dampaknya pada pemodelan prediktif dan pengambilan keputusan perawatan kesehatan.
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