IJBTT

An Efficient Classification of Congenital Fetal Heart Disorder using Improved Random Forest Algorithm

	International Journal of Engineering Trends and Technology (IJETT)
	© 2020 by IJETT Journal
	Volume-68 Issue-12
	Year of Publication : 2020
	Authors : K. Vimala, Dr. D. Usha
	DOI :  10.14445/22315381/IJETT-V68I12P229

Citation 

MLA Style: K. Vimala, Dr. D. Usha. An Efficient Classification of Congenital Fetal Heart Disorder using Improved Random Forest Algorithm International Journal of Engineering Trends and Technology 68.12(2020):182-186. 

APA Style:K. Vimala, Dr. D. Usha. An Efficient Classification of Congenital Fetal Heart Disorder using Improved Random Forest Algorithm  International Journal of Engineering Trends and Technology, 68(12), 182-186.

Abstract
Congenital genetic disorders are one of the major complications in the medical application. Congenital disorders can be detected in the earlier stage, and patience could be diagnosed as soon as possible. This research work deals with the identification and detection of fetal heart congenital genetic disorders in humans. The gene dataset consists of the fetus from 20-weeks of conception. The dataset is pre-processed to check null criteria, and gene selection is performed using Principal component analysis, where the features are reduced for further processing. The classification was carried out using Machine Learning algorithms such as Improved Random forest classifier, Support Vector Machine, and Gradient Boosting algorithm. The performance of the random forest classification provided the best result of 87.85%.

Reference
[1] O. Valenti, S. Monte, and E. Giorgio, Fetal cardiac function during the first trimester of pregnancy, An International Journal of prenatal Diagnosis and fetal Maternal Medicine. 5(3) (2011) 59-62.
[2] H. Li, J. Wei, and Y. Ma, Prenatal diagnosis of congenital fetal heart abnormalities and clinical analysis, Journal of Zhejiang University Science B, 6(9) (2005) 903-906.
[3] C.M.J. Tan, The Transitional Heart: From Early Embryonic and Fetal Development to Neonatal Life, Fetal Diagnosis, and Therapy. 47(5) (2019) 373-386.
[4] J.M. Matinez, M. Comas, and A. Borrell, Abnormal first?trimester ductus venous blood flow: a marker of cardiac defects in fetuses with normal karyotype nuchal translucency Ultrasound in Obstetrics and Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology. 35(3) (2010) 267-272.
[5] E.C.M. Nelissen, A.P.A. Van Montfoort, and L.J.M. Smits, IVF culture medium affects human intrauterine growth as early as the second trimester of pregnancy, Human Reproduction, 28 (8), (2013) 2067-2074.
[6] S. Marchiano, A. Bertero, and C.E. Murry Learn from Your Elders: Developmental Biology Lesson to Guide Maturation of Stem Cell- Derived Cardiomyocytes. Pediatric Cardiology. 40(7) (2019) 1367-1387.
[7] J.I. Iruretagoyena, W. Davis and C. Bird, Metabolic gene profile in early human fetal heart development, Molecular Human Reproduction. 20(7) (2014) 690–700.
[8] R. Cerychova and G. Pavlinkova, HIF-1, Metabolism, and Diabetes in the Embryonic and Adult Heart, Frontiers in Endocrinology. 9 (2018) 460.
[9] J.M. Walejko, J.P. Koelmel, and T.J. Garrett, Multiomics approach reveals metabolic changes in the heart at birth, American Journal of Physiology. 315(6) (2018) 1212-1223.
[10] Z. Geng and J. Wang, Microarray Analysis of Differential Gene Expression Profile between Human Fetal and Adult Heart. Pediatric Cardiology. 38 (2017) 700 -706.
[11] D.S. Dizon- Townson, J. Lu and T.K. Morgan, Genetic expression by fetal chorionic villi during the first trimester of human gestation, American Journal of Obstetrics Gynecology. 183( 3) (2000) 706-711.
[12] H. Li, S. Qin, and F. Xiao Predicting the first-trimester outcome of embryos with cardiac activity in women with recurrent spontaneous abortion, Journal of International Medical Research, 48(6) (2020).
[13] A.R. Singh, A. Sivadas, A. Sabharwal, S.K. Vellarikal, R. Jayarajan, A. Verma, S. Kapoor, A. Joshi, V. Scaria and S. Sivasubbu, Chamber Specific Gene Expression Landscape of the Zebrafish Heart, Plos One, 11, no. 1 0147823, 2016.
[14] T. Workalemahu, M. Ouidir, and D. Shrestha, Differential DNA Methylation in Placenta Associated with Maternal Blood Pressure During Pregnancy, Hypertension. 75(4) (2020) 1117 – 1124.
[15] B.A Firulli, R.M. George and J. Harkin, Hand1 loss-of-function within the embryonic reveals survivable congenital cardiac defects and adult heart failure. Cardiovascular Research, 116(3) (2020) 605-618.
[16] N. Velayutham and E.J. Agnew, Postnatal Cardiac Development and Regenerative Potential in Large Mammals. 40 (2019) 1345 – 1358.
[17] J. Binder, S. Carta, and J.S. Carvalho, Evidence for uteroplacental malperfusion in fetuses with a major congenital heart defect, Plos One. 15 (2) (2020) 0226741.
[18] M.A Khan, M Kar, S Mital, and S. Kumar, Small scale transcript expression profile of Human first trimester placental villi analyzed by a custom-tailored cDNA array, Indian Journal of Physiology and Pharmacology. 54(3) (2010) 235-254.
[19] K.Y. Yeung and W.L. Ruzzo, Principal component analysis for clustering gene expression data, Bioinformatics. 17(9) (2001) 763-771.
[20] S. Wang, J. Wang, and H. Chen, SVM-Based Tumor Classification with Gene Expression Data, Advanced Data Mining, and Applications. 2093 (2006) 864-870.
[21] O. Gonzalez-Recio and J.A. Jimenez-Montero, The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets, Journal of Dairy Science. 96(1) (2013) 614-624.
[22] R. Díaz-Uriarte and S.A. De Andres, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7(3) (2006)1471-2105.
[23] M. Ram, A. Najafi, and M.T. Shakeri, Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest, Iranian journal of Pathology.12(4) (2012) 339-347.

Keywords
Congenital Heart Defects, Principal Component Analysis, Improved Random Forest Algorithm.