Comparation analysis of naïve bayes and decision tree C4.5 for caesarean section prediction

Main Article Content

I Gusti Ayu Suciningsih
Muhammad Arif Hidayat
Renita Arianti Hapsari


The development of technology can be used to facilitate many matters. One of them is childbirth in the medical fields. Maternal mortality rate (MMR) is the number of maternal deaths during pregnancy to postpartum caused by pregnancy, childbirth or its management. There are several methods of labors that can be done. The determination of the labor is based on many factors and must be in accordance with the conditions of pregnant patient. Caesarean birth is the last alternative in labor, due to high risk factors. The objective of this research is to predicte and analyse caesarean section using C4.5 and Naïve Bayes classifier models. For experimentation the dataset is collected from UCI Machine Learning Repository and the main attributes represented in this dataset are age, delivery number, delivery time, blood of pressure, and heart problem. The accuracy using C4.5 by 80 training cases is 45% And the accuracy using Naïve Bayes is 50%.


Download data is not yet available.

Article Details

How to Cite
I. G. A. Suciningsih, M. A. Hidayat, and R. A. Hapsari, “Comparation analysis of naïve bayes and decision tree C4.5 for caesarean section prediction”, JOSCEX, vol. 2, no. 1, pp. 46-52, Mar. 2021.


H. Manik, M. F. G. Siregar, R. Kintoko Rochadi, E. Sudaryati, I. Yustina, and R. S. Triyoga, “Maternal mortality classification for health promotive in Dairi using machine learning approach,” IOP Conf. Ser. Mater. Sci. Eng., vol. 851, no. 1, 2020, doi: 10.1088/1757-899X/851/1/012055.

Anggorowati and N. Sudiharjani, “Mobilisasi Dini dan Penyembuhan Luka Operasi Pada Ibu Post Sectio Caesarea (SC) di Ruang Dahlia Rumah Sakit Umum Daerah Kota Salatiga,” Pros. Semin. Nas. dan Int. Univ. Muhammadiyah Semarang, pp. 30–35, 2010, [Online]. Available:

L. Andayasari et al., “Proporsi seksio sesarea dan faktor yang berhubungan dengan seksio sesarea di Proporsi Seksio Sesarea dan Faktor yang Berhubungan dengan Seksio Sesarea di Jakarta THE PROPORTION OF CAESAREAN SECTION AND ASSOCIATED FACTORS IN HOSPITAL OF JAKARTA,” pp. 6–16, 2014.

N. R. Indraswari and Y. I. Kurniawan, “Aplikasi Prediksi Usia Kelahiran Dengan Metode Naive Bayes,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 129–138, 2018, doi: 10.24176/simet.v9i1.1827.

P. Radha and B. Srinivasan, “Predicting Diabetes by cosequencing the various Data Mining Classification Techniques,” vol. 1, no. 6, pp. 334–339, 2014.

A. Kamat, V. Oswal, and M. Datar, “Implementation of Classification Algorithms to Predict Mode of Delivery,” Int. J. Comput. Sci. Inf. Technol., vol. 6, no. 5, pp. 4531–4534, 2015.

R. H. Saputra and B. Prasetyo, “Improve the Accuracy of C4 . 5 Algorithm Using Particle Swarm Optimization ( PSO ) Feature Selection and Bagging Technique in Breast Cancer Diagnosis,” pp. 47–55, 2020.

M. W. L. Moreira, J. J. P. C. Rodrigues, A. M. B. Oliveira, K. Saleem, and A. V. Neto, “An inference mechanism using Bayes-based classifiers in pregnancy care,” 2016 IEEE 18th Int. Conf. e-Health Networking, Appl. Serv. Heal. 2016, no. Dm, pp. 0–4, 2016, doi: 10.1109/HealthCom.2016.7749475.

I. E. Tiffani, “Optimization of Naïve Bayes Classifier By Implemented Unigram , Bigram , Trigram for Sentiment Analysis of Hotel Review,” pp. 1–7, 2020.

A. Wibowo, D. Manongga, and H. D. Purnomo, “The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation,” Sci. J. Informatics, vol. 7, no. 1, pp. 99–112, 2020, doi: 10.15294/sji.v7i1.24241.

A. De Ramón Fernández, D. Ruiz Fernández, and M. T. Prieto Sánchez, “A decision support system for predicting the treatment of ectopic pregnancies,” Int. J. Med. Inform., vol. 129, pp. 198–204, 2019, doi: 10.1016/j.ijmedinf.2019.06.002.

E. Fitriani, “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” Sistemasi, vol. 9, no. 1, p. 103, 2020, doi: 10.32520/stmsi.v9i1.596.

X. Chu, I. F. Ilyas, S. Krishnan, and J. Wang, “Data cleaning: Overview and emerging challenges,” Proc. ACM SIGMOD Int. Conf. Manag. Data, vol. 26-June-20, pp. 2201–2206, 2016, doi: 10.1145/2882903.2912574.