Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %. HIGHLIGHTS Proposed a dynamic model for ART outcome prediction Partial implementation of the model with the help of machine learning incremental classifier Performance evaluation of the model with the state-of-art methods.
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 HIGHLIGHTS• Proposed a hybrid feature ranking algorithm named VIGAREA• The proposed feature ranking algorithm gives importance to the individual feature as well as interaction between the features.• The proposed VIGAREA can combine any number and type of feature selection methods along with information gain ratio
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