A Machine Learning Pipeline to Optimally Utilize Limited Samples in Predictive Modeling

In many research studies, acquiring large amounts of data for predictive modeling can be expensive and time consuming. Many scientific pilot studies acquire only a limited number of data samples, which may be sufficient for statistical analysis but may not be ideal for using a standard machine learning pipeline. We have implemented and publicly shared a machine learning pipeline that aims to optimally utilize limited data samples in order to provide useful insights into the predictive performance and importance of the input variables. Our proposed pipeline uses support vector machine classifier models to simultaneously yield classification accuracy, a generalizable parametric formulation to classify individual samples, and an optimal way to identify the most predictive variable dimension and combination. These attributes of our pipeline also provide a useful intuition and interpretation of `black-box' machine learning models to facilitate many other fields of research. The initial version of the pipeline has been successful in a clinical research study with a limited sample size. In this paper, we package the complete version of the pipeline and demonstrate its utility and attributes using two publicly available datasets.