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Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest
Abstract
Abstrak
Diabetes adalah salah satu penyakit kronis yang mengancam jiwa dengan pertumbuhan tercepat yang telah mempengaruhi 422 juta orang di seluruh dunia menurut laporan Organisasi Kesehatan Dunia (WHO), pada tahun 2018. Diabetes dianggap sebagai salah satu penyakit paling mematikan dan kronis yang menyebabkan peningkatan gula darah. Banyak komplikasi terjadi jika diabetes tetap tidak diobati dan tidak teridentifikasi. Namun, peningkatan pendekatan machine learning memecahkan masalah kritis ini. Tujuan dari penelitian ini adalah merancang model yang dapat memprakirakan kemungkinan terjadinya diabetes pada pasien dengan ketelitian yang maksimal. Klasifikasi adalah teknik data mining yang menetapkan kategori pada kumpulan data untuk membantu dalam memprediksi dan analisis yang lebih akurat. Oleh karena itu tiga algoritma klasifikasi machine learning yaitu Suport Vector Machine, Naive Bayes dan Random Forest digunakan dalam percobaan ini untuk mendeteksi diabetes secara dini. Eksperimen dilakukan menggunakan dataset Diabetes Hospital in Sylhet, Bangladesh yang bersumber dari UCI repository. Performa ketiga algoritma dievaluasi pada berbagai ukuran seperti Precision, Accuracy, F-Measure, dan Recall. Akurasi diukur melalui instance yang diklasifikasikan dengan benar dan salah. Hasil yang diperoleh menunjukkan Random Forest mengungguli dengan nilai akurasi tertinggi 97,88% dibandingkan algoritma lain. Hasil ini diverifikasi menggunakan kurva Receiver Operating Characteristic (ROC) secara tepat dan sistematis.
Kata Kunci: diabetes, naive bayes, random forest, akurasi, support vector machine, machine learning
Abstract
Diabetes is one of the fastest growing, life-threatening chronic diseases affecting 422 million people worldwide, according to a report by the World Health Organization (WHO) in 2018. Diabetes is considered to be one of the most deadly and chronic diseases that cause elevated blood sugar. Many complications occur if diabetes remains untreated and unidentified. However, an improved machine learning approach solves this critical problem. The aim of this study is to design a model that can predict the likelihood of diabetes occurr in patients with maximum accuracy. Therefore, three machine learning classification algorithms, namely Support Vector Machine, Naive Bayes and Random Forest, were used in this experiment to detect diabetes early. Experiments were conducted using the Diabetes Hospital in Sylhet, Bangladesh dataset sourced from the UCI repository. The performance of the three algorithms is evaluated on various measures such as Precision, Accuracy, F-Measure, and Recall. Accuracy is measured through correctly and incorrectly classified instances. The results obtained showed that Random Forest outperformed with the highest accuracy value of 97.88% compared to other algorithms. These results are verified using the Receiver Operating Characteristic (ROC) curve accurately and systematically.
Keywords: diabetes, naive bayes, random forest, accuracy, machine learning, support vector machine
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DOI: https://doi.org/10.32520/stmsi.v10i1.1129
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