Comparison of Machine Learning Algorithms in Diabetes Risk Classification
DOI:
https://doi.org/10.57152/ijatis.v1i2.1141Keywords:
Confusion Matrix, Decision Tree, Diabetes, K-Nearest Neighbors, Logistic RegressionAbstract
Diabetes is a disease in which blood sugar levels are excessive without insulin control so that body functions do not function normally. Diabetes is also a disease that many people suffer from and is one of the main causes of death throughout the world. For this reason, we need to know the factors that are indicators of someone suffering from diabetes. This research compares the Decision Tree, Logistic Regression, and K-Nearest Neighbors algorithms with accuracy and Confusion Matrix parameters to determine diabetes sufferers in 520 data with the main indicator attributes supporting diabetes. From the test results of the three algorithms, the Decision Tree and K-Nearest Neighbors models have the highest accuracy of 86%. The Logistic Regression Algorithm has a fairly good accuracy of 83%.
References
A. Muliawati and H. Nurramdhani Irmanda, “Penerapan Borderline-SMOTE dan Grid Search pada Bagging-SVM untuk Klasifikasi Penyakit Diabetes,” 2022.
S. Anggraini, M. Akbar, A. Wijaya, H. Syaputra, and M. Sobri, “Klasifikasi Gejala Penyakit Coronavirus Disease 19 (COVID-19) Menggunakan Machine Learning,” Journal of Software Engineering Ampera, vol. 2, no. 1, pp. 57–68, Feb. 2021.
A. Ridwan, “Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus,” Jurnal Sistem Komputer dan Kecerdasan Buatan, vol. 4, no. 1, pp. 15–21, Oct. 2020.
H. Apriyani, “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus,” 2020. [Online]. Available: https://journal-computing.org/index.php/journal-ita/index
B. A. Candra Permana and I. K. Dewi Patwari, “Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes,” Infotek?: Jurnal Informatika dan Teknologi, vol. 4, no. 1, pp. 63–69, Jan. 2021, doi: 10.29408/jit.v4i1.2994.
H. A. Dwi Fasnuari, H. Yuana, and M. T. Chulkamdi, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Penyakit Diabetes Melitus,” Antivirus?: Jurnal Ilmiah Teknik Informatika, vol. 16, no. 2, pp. 133–142, Oct. 2022, doi: 10.35457/antivirus.v16i2.2445.
I. F. Nurahmadan, A. Agusta, P. A. Winarno, B. H. Sazali, Y. Thurfah, and A. Rosaliah, Perbandingan Algoritma Machine Learning Untuk Klasifikasi Denyut Jantung Janin. 2021.
D. Sartika and D. I. Sensuse, “Perbandingan Algoritma Klasifikasi Naive Bayes, Nearest Neighbour, dan Decision Tree pada Studi Kasus Pengambilan Keputusan Pemilihan Pola Pakaian,” Jatisi, vol. 1, no. 2, pp. 151–161, Mar. 2017.
A. Ilham Fatimah and S. Saepudin, “Penerapan Data Mining Dengan Metode Apriori Pada Penjualan Sembako (Studi Kasus: Grosir Sembako Lina),” 2022. [Online]. Available: https://rekayasa.nusaputra.ac.id/index
D. P. Utomo and M. Mesran, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,” Jurnal Media Informatika Budidarma, vol. 4, no. 2, p. 437, Apr. 2020, doi: 10.30865/mib.v4i2.2080.
I. Kadek, J. Arta, G. Indrawan, G. R. Dantes, P. Studi, and I. Komputer, “Data Mining Rekomendasi Calon Mahasiswa Berprestasi Di Stmik Denpasar Menggunakan Metode Technique For Others Reference By Similarity To Ideal Solution,” 2016.
Sri Diantika, Windu Gata, Hiya Nalatissifa, and Mareanus Lase, “Komparasi Algoritma SVM Dan Naive Bayes Untuk Klasifikasi Kestabilan Jaringan Listrik,” Jurnal Ilmiah Elektronika Dan Komputer, vol. Vol.14, no. No.1, pp. 10–15, Oct. 2021.
S. Ramadani, N. Zannah, S. Ayu, N. Nurhayati, F. Azzahra, and A. P. Windarto, “Analisis Data Mining Naive Bayes Klasifikasi Pada Kelayakan Penerima PKH,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 4, no. 1, pp. 374–381, 2020, doi: 10.30865/komik.v4i1.2725.
M. Gunawan, M. Zarlis, and R. Roslina, “Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal Media Informatika Budidarma, vol. 5, no. 2, p. 513, Apr. 2021, doi: 10.30865/mib.v5i2.2925.
R. Saxena, S. K. Sharma, M. Gupta, and G. C. Sampada, “A Novel Approach for Feature Selection and Classification of Diabetes Mellitus: Machine Learning Methods,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/3820360.
V. Chang, J. Bailey, Q. A. Xu, and Z. Sun, “Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms,” Neural Comput Appl, vol. 35, no. 22, pp. 16157–16173, Aug. 2023, doi: 10.1007/s00521-022-07049-z.
A. B. Amjoud and M. Amrouch, “Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression,” IEEE Access, vol. 10, pp. 128543–128553, 2022, doi: 10.1109/ACCESS.2022.3225455.
C. Krishna Suryadevara, “Issue 4 Diabetes Risk Assessment Using Machine Learning: A Comparative Study Of Classification Algorithms,” 2023. [Online]. Available: www.iejrd.com
R. A. Husen, R. Astuti, L. Marlia, R. Rahmaddeni, and L. Efrizoni, “Analisis Sentimen Opini Publik pada Twitter Terhadap Bank BSI Menggunakan Algoritma Machine Learning,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 211–218, Oct. 2023, doi: 10.57152/malcom.v3i2.901.
J. J. Khanam and S. Y. Foo, “A comparison of machine learning algorithms for diabetes prediction,” ICT Express, vol. 7, no. 4, pp. 432–439, Dec. 2021, doi: 10.1016/j.icte.2021.02.004.
A. Nurjulianty and H. Darwis, “Jurnal Media Informatika Budidarma Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal,” Jurnal Media Informatika Budidarma, vol. 7, pp. 1740–1748, 2023, doi: 10.30865/mib.v7i4.6262.
T. M. Le, T. M. Vo, T. N. Pham, and S. V. T. Dao, “A Novel Wrapper-Based Feature Selection for Early Diabetes Prediction Enhanced with a Metaheuristic,” IEEE Access, vol. 9, pp. 7869–7884, 2021, doi: 10.1109/ACCESS.2020.3047942.
A. W. Sari, T. I. Hermanto, and M. Defriani, “Sentiment Analysis Of Tourist Reviews Using K-Nearest Neighbors Algorithm And Support Vector Machine,” Sinkron, vol. 8, no. 3, pp. 1366–1378, Jul. 2023, doi: 10.33395/sinkron.v8i3.12447.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” 2021.
Y. I. Kurniawan, “Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 4, p. 455, Oct. 2018, doi: 10.25126/jtiik.201854803.