* Corresponding Author
Hide
Show all
Classification Of Maternal Health Risk Using Three Models Naive Bayes Method
https://doi.org/10.22146/ijccs.84242 Nurul Fathanah Mustamin(1), Firman Aziz(2*), Firmansyah Firmansyah(3), Pertiwi Ishak(4)
(1) Information Technology, Lambung Mangkurat University, Banjarmasin
(2) Faculty of Mathematics and Natural Science, Pancasakti University, Makassar
(3) Faculty of Mathematics and Natural Science, Pancasakti University, Makassar
(4) Faculty of Mathematics and Natural Science, Pancasakti University, Makassar
(*) Corresponding Author
Abstract
Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.
Keywords
Full Text:
PDFReferences
A. N.- BIMIPA and undefined 2013, “Klasifikasi Naïve Bayes untuk Prediksi Kelahiran pada Data Ibu Hamil,” journal.ugm.ac.id, vol. 23, no. 3, 2013, Accessed: Jan. 27, 2023. [Online]. Available: https://journal.ugm.ac.id/bimipa/article/view/13859. [2] Q. Hasanah, A. Andrianto, M. H.-B. Sainstek, and undefined 2018, “Sistem Informasi Posyandu Ibu Hamil dengan Penerapan Klasifikasi Resiko Kehamilan Menggunakan Metode Naïve Bayes,” jurnal.unej.ac.id, Accessed: Jan. 27, 2023. [Online]. Available: https://jurnal.unej.ac.id/index.php/BST/article/view/7554. [3] M. Ahmed, M. K.-2020 2nd I. C. on, and undefined 2020, “IoT based risk level prediction model for maternal health care in the context of Bangladesh,” ieeexplore.ieee.org, doi: 10.1109/STI50764.2020.9350320. [4] M. H.-I. I. Journal and undefined 2021, “Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree,” jurnal.unej.ac.id, vol. 6, no. 3, p. 177, 2021, Accessed: Jan. 27, 2023. [Online]. Available: https://jurnal.unej.ac.id/index.php/INFORMAL/article/view/28396. [5] J. Homepage, B. Delvika, S. Nurhidayarnis, P. D. Rinada, N. Abror, and A. Hidayat, “Perbandingan Klasifikasi Antara Naive Bayes dan K-Nearest Neighbor Terhadap Resiko Diabetes pada Ibu Hamil: Comparison of Classification Between Naive,” journal.irpi.or.id, vol. 2, pp. 68–75, 2022, Accessed: Jan. 27, 2023. [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/view/432. [6] M. G.-R. with children and young people and undefined 2009, “Data collection and analysis,” books.google.com, Accessed: Jan. 29, 2023. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=DVBrL1UiatcC&oi=fnd&pg=PA65&dq=data+collection&ots=qv8L5s9o7b&sig=gAVcNOmYsXGEtIw2ZCH5bgJYRS8. [7] L. Breiman, “Random Forests,” Mach. Learn. 2001 451, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324. [8] S. García, S. Ramírez-Gallego, J. Luengo, J. M. Benítez, and F. Herrera, “Big data preprocessing: methods and prospects,” Big Data Anal., vol. 1, no. 1, Dec. 2016, doi: 10.1186/S41044-016-0014-0. [9] S. Wager, “Cross-Validation, Risk Estimation, and Model Selection,” Sep. 2019, Accessed: Jan. 29, 2023. [Online]. Available: http://arxiv.org/abs/1909.11696. [10] S. Chen, G. Webb, L. Liu, X. M.-K.-B. Systems, and undefined 2020, “A novel selective naïve Bayes algorithm,” Elsevier, Accessed: Jan. 29, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950705119306185. [11] Z. Bi, Y. Han, C. Huang, M. W.-2019 I. Conference, and undefined 2019, “Gaussian naive Bayesian data classification model based on clustering algorithm,” atlantis-press.com, 2019, Accessed: Jan. 29, 2023. [Online]. Available: https://www.atlantis-press.com/proceedings/masta-19/125913250. [12] G. Singh, B. Kumar, … L. G.-… C. on A., and undefined 2019, “Comparison between multinomial and Bernoulli naïve Bayes for text classification,” ieeexplore.ieee.org, Accessed: Jan. 29, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8776800/. [13] M. Singh, M. Bhatt, H. Bedi, U. M.-M. T. Proceedings, and undefined 2020, “Performance of bernoulli’s naive bayes classifier in the detection of fake news,” Elsevier, Accessed: Jan. 29, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2214785320385333. [14] M. A.-P. C. Science and undefined 2021, “Review the performance of the Bernoulli Naïve Bayes Classifier in Intrusion Detection Systems using Recursive Feature Elimination with Cross-validated selection of,” Elsevier, Accessed: Jan. 29, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050921013156. [15] F. Provost, R. K.-M. learning, and undefined 1998, “Guest editors’ introduction: On applied research in machine learning,” academia.edu, Accessed: Mar. 08, 2022. [Online]. Available: https://www.academia.edu/download/40088606/Guest_Editors_Introduction_On_Applied_R20151116-31348-ccsgqm.pdf.
DOI: https://doi.org/10.22146/ijccs.84242
Article Metrics
Abstract views : 1187
| 
views : 765
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1