Prediksi Penyakit Diabetes Dengan Naive Bayes

Yuni Wardana -
Devni Sari -

Abstract


Diabetes is one of the top ranked diseases for non-communicable diseases which is top cause of death worldwide. Every year 442 million people worldwide have diabetes and 1.6 million people die due to diabetes. With increasing cases of diabetes every year, this detection may need to be done before diabetes occurs. So a research was conducted on diabetes prediction using the Naïve Bayes method. According to the Naïve Bayes model, people with diabetes have a 28% chance of developing diabetes and a 72% chance of not having diabetes. And this research achieved 94% accuracy, 95% precision, and 98% recall.


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References


S. N. R. Toharin, M. Widya Hary Cahyati S.KM and d. I. Z. MH.Kes, "Hubungan Moditifikasi Gaya Hidup Dan Kepatuhan Konsumsi Obat Antidiabetik Dengan Kadar Gula Darah Pada Penderita Diabetes Melitus Tipe 2 Di RS QIM Batang Tahun 2013," UNNES JOURNAL OF PUBLIC HEALTH, vol. 4, no. 2, 2015.

Y. Zhang, T. M. D. D Ph, S. E. Mann, Y. Chen, J. Martin, V. Moore, A. Baldwin, V. A. Reidel and W. W. Quick, "The Ecomomic Costs of Udiagnosed Diabetes," The Economic Cost Of Undiagnosed Diabetes 12, 2009.

A. Bulu, T. D. Wahyuni and A. Sutriningsih, "Hubungan Antara Tingkat Kepatuhan Minum Obat Dengan Kadar Gula Darah Pada Pasien Diabetes Melitus Tipe II," Nursing News: Jurnal Ilimiah Keparawatan, 2019.

N. L. U. Sumadewi, Isolasi Senyawa Tanin dan Uji Efek Hipogllikemik Ekstrak Kulit Batang Bungur (Lagerstroemia Speciosa Pers.) Terhadap Darah Mencit Yang diinduksi Aloksan, Bali, Indonesia: Unud Library, 2011.

Eniarti, Sardjito Menyata, 2021.

N. Cho, J. Shaw, S. Karuranga, Y. Huang, J. d. R. Fernandes, A. Ohlogge and B. Malanda, "IDF Diabetes Atlas: Global Estimates Of Diabetes Prevalence For 2017 And Projections For 2015," Diabetes Research and Clinical Practice, pp. 271-281, 2018.

IDF, "International Diabetes Federation : Diabetes Atlas Ninth Edition," USA, 2019.

R. D. Joshi and C. K. Dhakal, "Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches," International Journal of Environmental Research and Public Health, 2021.

C. Zhu, C. U. Idemudia and W. Feng, "Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques," Informatics in Medicine Unlocked, 2019.

R. Dankner, N. Agay, L. Olmer, H. Murad, L. K. Boker, R. D. Balicer and L. S. Freedman, "Metformin Treatment and Cancer Risk: Cox Regression Analysis with Time-Dependent Covariates of 320,000 Individuals with Incident Diabetes Mellitus," Am J Epidemiol, 2019.

A. Mukasheva, "The Prevalence of Diabetes in the Republic of Kazakhstan Based on Regression Analysis Methods," International Journal of Health and Medical Sciences, 2019.

N. Arora, A. Singh, M. Z. N. Al-Dabagh and S. K. Maitra, "A Novel Architecture for Diabetes Patients’ Prediction Using K-Means Clustering and SVM," Mathematical Problems in Engineering, 2022.

M. Raihan, M. T. Islam, F. Farzana, M. G. M. Raju and H. S. Mondal, "An Empirical Study to Predict Diabetes Mellitus using K-Means and Hierarchical Clustering Techniques," in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019.

A. Anggrawan and M. Mayadi, "Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes," Jurnal : Manajemen, Teknik Informatika dan Rekayasa Komputer, 2023.

G. V. R, D. J. N and D. A. A. K. N, "Classification Of Gestational Diabetes Using Modified Fuzzy C Means Clustering And Machine Learning Technique," Turkish Journal of Computer and Mathematics Education, 2021.

M. Maniruzzaman, M. M. Islam, M. J. Rahman, M. A. M. Hasan and J. Shin, "Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data," Diabetes and Metabolic Syndrome: Clinical Research and Review, 2021.

S. K. Das, T. S. Bhattacharya, M. Ghosh and J. Chowdhury, "Probing blood plasma samples for the detection of diabetes using SERS aided by PCA and LDA multivariate data analyses," New Journal of Chemistry, 2021.

R. D. H. Devi, A. Bai and N. Nagarajan, "A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms," Obesity Medicine, 2020.

N. Mohan and V. Jain, "Performance Analysis of Support Vector Machine in Diabetes Prediction Diabetes Prediction," in 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2020.

Wisdayani, D. Selvy and dkk, "Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes untuk Klasifikasi Tingkat Keparahan Korban Kecelakaan Lalu Lintas di Kabupaten Pati Jawa Tengah," Skripsi Universitas Muhammadiyah Semarang, 2019.

M. Windariti, "Perbandingan Kinerja Algoritma Naïve Bayes dan Bayesian Network dalam Klasifikasi Masa Studi Mahasiswa," Prosiding Seminar Nasional Aplikasi Sains & Teknologi, 2018.

E. Prasetyo, Data Mining : Konsep dan Aplikasi Menggunakan Matlab, Yogyakarta: Andi Offset, 2021.

"Predict diabetes based on diagnostic measures | Kaggle," https://www.kaggle.com/datasets/houcembenmansour/predict-diabetes-based-on-diagnostic-measures (accessed 02 April 2023).

Y. Mukhtar, U. M. Yunusa and G. A.M, "A Modern Overview on Diabetes Mellitus: A Choric Endocrine Disorder," European Journal of Biologi, pp. 1-14, 2020.

Y. Yu, O. A. Arah, Z. Liew, S. Cnattingius, J. Olsen, H. T. Sørensen, G. Qin and J. Li, "Maternal diabetes during pregnancy and early onset of cardiovascular disease in offspring: population based cohort study with 40 years of follow-up," BMJ, 2019.




DOI: http://dx.doi.org/10.24036/unpjomath.v8i3.15070