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

  IJETT-book-cover  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


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.

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%.

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