Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

• By Puspitasari, Diah; Ramanda, Kresna; Supriyatna, Adi; Wahyudi, Mochamad; Sikumbang, Erma Delima; Sukmana, Sulaeman Hadi;
• Seminar Internasional Bereputasi
• 379
• 1-6

Premature birth is still a big problem in Indonesia, in general, 15 million babies
are born prematurely every year, more than 1 million babies die from complications due to
premature birth.The main purpose of this study is to compare the Artificial Neural Network
and Naive Bayes datamining algorithm models to predict preterm birth so as to obtain clinical
evidence in preterm birth long before confinement so that sudden preterm birth can be converted
to normal nativity. The model proposed in research on the prediction of preterm birth is by
applying an Artificial Neural Network (ANN) algorithm and Naive Bayes algorithm. Where the
two algorithms will be compared the level of accuracy and the value of the AUC against the
prediction of preterm birth The results obtained that the prediction of preterm birth using the
Artificial Neural Network (ANN) algorithm produces an accuracy value of 90.67% and an ROC
value of 0.954. While the Naive Bayes algorithm produces an accuracy value of 84.53% and an
ROC value of 0.929. For this reason, it can be concluded that the Artificial Neural Network
(ANN) algorithm has a superior accuracy of 6.14% and 0.025 for its ROC value in predicting
preterm birth.

• Katakunci -
• URL https://iopscience.iop.org/article/10.1088/1742-6596/1641/1/012068
• Penerbit IOP Conference Series: Materials Science and Engineering,

REFERENSI

pple-interchange-newline"> [1] Carolina I and Kresna R, 2018 Klasifikasi kelahiran prematur menggunakan algoritma c4.5 p. 668–672.
[2] Esty A Frize M Gilchrist J and Bariciak E, 2018 Applying Data Prepro cessing Metho ds to Predict Premature
Birth in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology
So ciety (EMB C) 2018 p. 6096–6099.
[3] Ko cak A T and Yilmaz A, 2018 Segmentation and classification of contractions in uterine 16 channels EMG
signals for preterm birth prediction in 2018 26th Signal Pro cessing and Communications Applications
Conference (SIU) p. 1–4.
[4] Goldenb erg R L Culhane J F Iams J D and Romero R, Jan. 2008 Epidemiology and causes of preterm birth
Lancet 371, 9606 p. 75–84.
[5] Raghav H K V S Devi S P Rengara j N and Thanranikumar E, 2018 Prediction of Preterm Pregnancies using
Soft Computing Techniques Neural Networks and Gradient Descent Optimizer 2018 Int. Conf. Comput.
Commun. Informatics March 2016 p. 1–4.
[6] Janjarasj itt S, 2017 Evaluation of p erformance on preterm birth classification using single wavelet-based
features of EHG signals in 2017 10th Biomedical Engineering International Conference (BMEiCON)
2017–Janua p. 1–4.
[7] Gorunescu F, 2011 Data Mining Concepts , Mo dels and Te chniques Springer.
[8] Catley C Frize M Walker R C and Petriu D C, Jul. 2006 Predicting High-Risk Preterm Birth Using Artificial
Neural Networks IEEE Trans. Inf. Technol. Biomed. 10, 3 p. 540–549.
[9] Septiani W D, 2017 Komparasi Meto de Klasifikasi Data Mining Algoritma C4.5 Dan Naive Bayes Untuk
Prediksi Penyakit Hepatitis J. Pilar Nusa Mandiri 13, 1 p. 76–84.
[10] Pari R Sandhya M and Sankar S, 2017 Risk factors based classification for accurate prediction of the Preterm
Birth in 2017 International Conf erence on Inventive Computing and Informatics (ICICI) Icici p. 394–399.
[11] Puspita A and Wahyudi M, 2015 Algoritma C4.5 Berbasis Decision Tree untuk Prediksi Kelahiran Bayi
Prematur K onf . Nasinal Ilmu Pengetah. dan Teknol. 1, 1 p. 97–102.
[12] Idowu I O Fergus P Hussain A Dobbins C and Askar H Al, 2014 Advance Artificial Neural Network
Classification Techniques Using EHG for Detecting Preterm Births in 2014 Eighth International Conference
on Complex, Intelligent and Software Intensive Systems p. 95–100.
[13] Ren J Lee S D Chen X Kao B Cheng R and Cheung D, 2009 Naive Bayes Classification of Uncertain Data
in 2009 Ninth IEEE International Conference on Data Mining 60703110 p. 944–949.