An Ensemble of Heterogeneous Incremental Classifiers for Assisted Reproductive Technology Outcome Prediction

Machine learning (ML) is a futuristic concept, utilized as a tool for modeling real-world applications. Today, healthcare worldwide has drawn the attention of ML, with its ability to analyze huge data sets and convert information into clinical insights that aid physicians in disease diagnosis and treatment planning, leading to low cost, better outcomes, and greater patient satisfaction. One of such medical applications calling for ML interventions is human infertility; an issue addressed by a set of medical procedures termed assisted reproductive technology (ART). The ART success rate, however, is very low because it is affected by a number of variables. ML techniques are now applied to predict ART outcomes to find strategies for an improved success rate. The literature reviewed on the subject shows that most of the available ART models are static. If ML is to have a role in healthcare, it must take an incremental, rather than static, approach. Therefore, this research proposes a dynamic model for ART outcome prediction. The model is built by an ensemble of two incremental classifiers, namely, the instance-based (IB1) learner and averaged one-dependence estimators (A1DEs) updatable learner through voting. The performance of the proposed model is checked with other ensemble models and ART data sets to find that the former shows promise in ART outcome prediction.