Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes
DOI:
https://doi.org/10.30812/matrik.v22i2.2777Keywords:
Diabetes, Machine Learning, Fuzzy C-means, K-Nearest NeighborAbstract
The disease is a common thing in humans. Diseases that attack humans do not know anyone and do not know age. The disease experienced by a person starts from an ordinary level until it can be declared severe to the point of being at risk of death. In this study, the early diagnosis was carried out related to diabetes, where diabetes is a condition in which the sufferer’s body has low sugar levels above normal. Symptoms experienced by sufferers include frequent thirst, frequent urination, frequent hunger, and weight loss. Based on these problems, a system is needed that can quickly find out the diagnosis experienced by a patient. This research aimed to diagnose diabetes early on based on early symptoms. The methods used are KNN and web-based fuzzy C-means. Creating a web-based system can represent medical personnel experts in a fast-diagnosing approach to diabetes. This system was a computer program embedded with the knowledge of the characteristics of diabetes. The results of testing the KNN and Fuzzy C-means applications and methods get an accuracy of 96% for the KNearest Neighbor method, while for the Fuzzy C-Means method with Confusion Matrix calculations, an accuracy of 96% is obtained, so it can be concluded that the Fuzzy C-means method Means better than the K-Nearest Neighbor method.
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Sains dan Informatika, vol. 3, no. 1, pp. 44–52, 2021.
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Elsevier Inc., 2015.
[5] F. M. Hana, “Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5,†Jurnal SISKOM-KB (Sistem
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[6] E. Cousin et al., “Diabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study
2019,†The Lancet Diabetes and Endocrinology, vol. 10, no. 3, pp. 177–192, 2022.
[7] P. Subarkah, I. Santiko, and A. Tri, “Perbandingan Kinerja Algoritma Cart dan Naive Bayesian untuk Mendiagnosa Penyakit
Diabetes Melitus,†Conference on Information Technology, Information System and Electrical Engineering, p. 17, 2017.
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disease in childhood, vol. 101, no. 9, p. e2, sep 2016.
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a Paediatric Department Lessons Learned,†Archives of Disease in Childhood, vol. 101, no. 9, pp. e2—-e2, 2016. [Online].
Available: https://adc.bmj.com/content/101/9/e2.13
[10] A. Anggrawan et al., “Machine Learning for Diagnosing Drug Users and Types of Drugs Used,†International Journal of
Advanced Computer Science and Applications, vol. 12, no. 11, pp. 111–118, 2021.
[11] F. Aris and Benyamin, “Penerapan Data Mining untuk Identifikasi Penyakit Diabetes Melitus dengan Menggunakan Metode
Klasifikasi,†Router Research, vol. 1, no. 1, pp. 1–6, 2019.
[12] I. P. D. Lesmana, “Pengembangan Decision Tree J48 Untuk Diagnosis Penyakit Diabetes Mellitus,†Seminar Nasional Teknologi
Informasi & Komunikasi Terapan, vol. 2, no. 1, p. 23, 2012.
[13] P. Achenbach et al., “A Classification and Regression Tree Analysis Identifies Subgroups of Childhood Type 1 Diabetes,â€
eBioMedicine, vol. 82, p. 104118, 2022. [Online]. Available: https://doi.org/10.1016/j.ebiom.2022.104118
[14] N. Dwitiyanti, N. Selvia, and F. R. Andrari, “Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia
Menurut Indikator Kesejahteraan Rakyat,†Faktor Exacta, vol. 12, no. 3, p. 201, 2019.
[15] Y. Ambar, Kusrini, and Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten
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[16] N. G. A. Dasriani, M. Mayadi, and A. Anggrawan, “Klasterisasi Lokasi Promosi PMB Dengan Fuzzy C-means Masa Pandemi
Covid 19,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 327–336, 2022.
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and Theoretical Nanoscience, vol. 16, no. 10, pp. 4425–4430, 2019.
[20] P. Guleria, S. Ahmed, A. Alhumam, and P. N. Srinivasu, “Empirical Study on Classifiers for Earlier Prediction of COVID-19
Infection Cure and Death Rate in the Indian States,†Healthcare (Switzerland), vol. 10, no. 1, 2022.
[21] A. Yosipof, R. C. Guedes, and A. T. Garc´ıa-sosa, “Data Mining and Machine Learning Models for Predicting Drug Likeness
and Their Disease or Organ Category,†Frontiers in Chemistry, vol. 6, no. May, pp. 1–11, 2018.
[22] J. Bullock, A. Luccioni, K. H. Pham, and M. Luengo-oroz, “Mapping the Landscape of Artificial Intelligence Applications
against COVID-19,†Journal of Artificial Intelligence Research, vol. 69, no. 1, pp. 807–845, 2020.
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BMJ, vol. 369, pp. 1–22, 2020.
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Letters, vol. 15, no. 4, pp. 319–326, 2021.
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Risk Nomogram in Wuhan and Guangdong, China,†The Journals of Gerontology Series A: Biological Sciences and Medical
Sciences, vol. 0813, no. April, pp. 1–11, 2018.
[26] X. Jiang et al., “Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity,†Computers,
Materials and Continua, vol. 63, no. 1, pp. 537–551, 2020.
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the early stage,†Journal of Translational Medicine, vol. 18, no. 1, pp. 1–12, 2020. [Online]. Available:
https://doi.org/10.1186/s12967-020-02374-0
[28] K. A. Tikki, H. H. Jaber, and A. A. Mahdi, “Estimation of Serum Uric Acid in Type II Diabetic Patients and Relationship with
Anemia,†HIV Nursing, vol. 22, no. 2, pp. 1523–1526, 2022.
[29] M. Rahman, D. Alamsah, M. Darmawidjadja, and I. Nurma, “Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode
Bayesian Regularization Neural Network (RBNN),†Jurnal Informatika, vol. 11, no. 1, p. 36, 2017.
[30] E. Purwaningsih and E. Nurelasari, “Penerapan K-Nearest Neighbor Untuk Klasifikasi Tingkat Kelulusan Pada Siswa,†Syntax
: Jurnal Informatika, vol. 10, no. 01, pp. 46–56, 2021.
[31] A. W. Fadillah, A. Tejawati, and N. Puspitasari, “Penerapan Fuzzy C-Means Pada Curah Hujan Di Kalimantan Timur,†Jurnal
Rekayasa Teknologi Informasi (JURTI), vol. 2, no. 1, p. 82, 2018.
[32] R. J. Sarjanako, “Penerapan Fuzzy C-Means Clustering Untuk Mengoptimalkan Penentuan Media Promosi,†Teknois : Jurnal
Ilmiah Teknologi Informasi dan Sains, vol. 6, no. 1, pp. 29–37, 2019.
[33] A. Anggrawan, N. Ibrahim, S. Muslim, and C. Satria, “Interaction between learning style and gender in mixed learning with
40and 60Science and Applications, vol. 10, no. 5, pp. 407–413, 2019.
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2023-03-31
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A. Anggrawan and M. Mayadi, “Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes”, MATRIK, vol. 22, no. 2, pp. 405–418, Mar. 2023, doi: 10.30812/matrik.v22i2.2777.
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