Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization
Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria. Â The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.
Fukui A, Yokota M, Funamizu A, Nakamua R, Fukuhara R, Yamada K, Kimura H, Fukuyama A, Kamoi M, Tanaka K, Mizunuma H. Changes of NK cells in preeclampsia. American journal of reproductive immunology. 2012 Apr 1;67(4):278-86.
Roberts JM, Gammill HS. Preeclampsia: recent insights. Hypertension. 2005 Dec 1;46(6):1243-9.
Wicaksono AP, Badriyah T, Basuki A. Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes. EMITTER International Journal of Engineering Technology. 2016 Aug 3;4(1):164-78.
Fahrudin TM, Syarif I, Barakbah AR. Data Mining Approach for Breast Cancer Patient Recovery. EMITTER International Journal of Engineering Technology. 2017 Jul 23;5(1):36-71.
Moreira MW, Rodrigues JJ, Oliveira AM, Ramos RF, Saleem K. A preeclampsia diagnosis approach using bayesian networks. InCommunications (ICC), 2016 IEEE International Conference on 2016 May 22 (pp. 1-5). IEEE.
Moreira MW, Rodrigues JJ, Oliveira AM, Saleem K, Neto AV. An inference mechanism using bayes-based classifiers in pregnancy care. Ine-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on 2016 Sep 14 (pp. 1-5). IEEE.
Tejera E, Jose areias M, Rodrigues A, Ramoa A, Manuel nieto-villar J, Rebelo I. Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. The Journal of Maternal-Fetal & Neonatal Medicine. 2011 Sep 1;24(9):1147-51.
Saha S, Biswas S, Acharyya S. Gene selection by sample classification using k nearest neighbor and meta-heuristic algorithms. InAdvanced Computing (IACC), 2016 IEEE 6th International Conference on 2016 Feb 27 (pp. 250-255). IEEE.
Neocleous CK, Anastasopoulos P, Nikolaides KH, Schizas CN, Neokleous KC. Neural networks to estimate the risk for preeclampsia occurrence. In Neural Networks, 2009. IJCNN 2009. International Joint Conference on 2009 Jun 14 (pp. 2221-2225). IEEE.
Moreira MW, Rodrigues JJ, Oliveira AM, Saleem K, Neto AJ. Predicting hypertensive disorders in high-risk pregnancy using the random forest approach. InCommunications (ICC), 2017 IEEE International Conference on 2017 May 21 (pp. 1-5). IEEE.
Cheng W, Fang L, Yang L, Zhao H, Wang P, Yan J. Varying coefficient models for analyzing the effects of risk factors on pregnant women's blood pressure. InMachine Learning and Applications (ICMLA), 2014 13th International Conference on 2014 Dec 3 (pp. 55-60). IEEE.
Roberts JM, Pearson G, Cutler J, Lindheimer M. Summary of the NHLBI working group on research on hypertension during pregnancy. Hypertension. 2003 Mar 1;41(3):437-45.
Nasional BP, Nasional BP. Laporan pencapaian tujuan pembangunan milenium di indonesia 2011. Meningkatkan Kesehatan Ibu. 2012:53-66.
Rahm E, Do HH. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull.. 2000 Dec;23(4):3-13.
Othman ZA, Bakar AA, Hamdan AR, Omar K, Shuib NL. Agent based preprocessing. InIntelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on 2007 Nov 25 (pp. 219-223). IEEE.
Tjiong AS, Monteiro ST. Feature selection with PSO and kernel methods for hyperspectral classification. InEvolutionary Computation (CEC), 2011 IEEE Congress on 2011 Jun 5 (pp. 1762-1769). IEEE.
Schuh MA, Angryk RA, Sheppard JW. Evolving Kernel Functions with Particle Swarms and Genetic Programming. InFLAIRS Conference 2012 May 16.
Haykin S, Network N. A comprehensive foundation. Neural networks. 2004 Feb;2(2004):41.
Witten IH, Frank E, Hall MA, Pal CJ. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann; 2016 Oct 1.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. InAdvances in neural information processing systems 2012 (pp. 1097-1105).
Zeiler MD, Krishnan D, Taylor GW, Fergus R. Deconvolutional networks. InComputer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on 2010 Jun 13 (pp. 2528-2535). IEEE.
Jwo DJ, Chang SC. Particle swarm optimization for GPS navigation Kalman filter adaptation. Aircraft Engineering and Aerospace Technology. 2009 Jul 3;81(4):343-52.
Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. InProceedings of the 23rd international conference on Machine learning 2006 Jun 25 (pp. 233-240). ACM.
Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering. 2007 Jun 10;160:3-24.
Williams N, Zander S, Armitage G. A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Computer Communication Review. 2006 Oct 10;36(5):5-16.
Copyright (c) 2018 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Plagiarism screening will be conducted by EMITTER Journal Editorial Board using iThenticate Plagiarism Checker and CrossCheck plagiarism screening service. Author should download and signing declaration of plagiarism form here and resubmit it with copyright transfer form via online submission.