Controlling Informative Features for Improved Accuracy and Faster Predictions in Omentum Cancer Models

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel feature detection and engineering machine-learning framework is presented to address this need. First, the Rip Curl process is applied which generates a set of 10 additional features. Second, we rank all features including the Rip Curl features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are used in model building. This process creates for more expressive features which are captured in models with an eye towards the model learning from increasing sample amount and the accuracy/time results. The performance of the proposed Rip Curl classification framework was tested on omentum cancer data. Rip Curl outperformed other more sophisticated classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.