A Hybrid Feature Ranking Algorithm for Assisted Reproductive Technology Outcome Prediction

• Autor(a) principal: Kothandaraman,Ranjini
• Data de Publicação: 2022
• Outros Autores: Andavar,Suruliandi, Raj,Raja Soosaimarian Peter
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Archives of Biology and Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100613
• topic: Assisted Reproductive Technology (ART); Ensemble Learner; Incremental Classifiers; Feature Ranking

Abstract In recent years, the emerging technology of machine learning has made vast strides in medicine. Machine learning-based clinical decision support systems assist doctors make efficient diagnoses and offer better prescriptions. Today, one of the greatest challenges for doctors worldwide is the treatment of infertility, with even the most sophisticated technology offering limited success. Currently, the Assisted Reproductive Technology (ART) in use is highly sophisticated technology that offers a success rate of 20%, depending on a slew of factors with complex relationships. With their capacity to analyze large and complex datasets, the application of machine learning techniques to predictions can maximize the ART success rate. This research work attempts a dynamic model for ART outcome prediction using incremental classifiernamed Ensemble of Heterogeneous Incremental Classifier (EHIC) in Machine Learning. In this paper,a new feature ranking algorithm named Voted Information Gain Attribute Rank Estimation Algorithm (VIGAREA) is proposed to enhance the performance of EHIC. The proposed VIGAREA is a combination of a number of feature selection methods and information gain ratio of each variable. It has the capability to rank the features based on its significance. The methodology and the way how the proposed VIGAREA is developed is presented. Experimental results proved that the EHIC with the proposed VIGAREA achieves the highest prediction with the ROC area of 95.5% for the ART dataset used for the research. The effectiveness of the proposed VIGAREA is checked with a range of miscellaneous feature selection methods and found that the proposed feature ranking method VIGAREA performs optimally. Further, the performance of the proposed model is compared to that of existing models, and the findings show that the former outperforms the latter.