On Effective Use of Feature Engineering for Improving the Predictive Capability of Machine Learning Models

Abstract

The utility of predictive models for the prognosis of the asthma disease that rely on clinical history and findings has been on constant rise owing to the attempts to achieve better disease outcomes through improved clinical processes. In this paper, a dataset containing a mix of both clinical findings from the pulmonary function test data and the clinical symptoms was used for the validation of the proposed prognostic model. The dataset is mixed in that it contains information on both symptomatic representations and medical history in the form of categorical data along with lung function parameters estimated using spirometer (with the data basically being quantitative (numerical) in nature). Feature engineering involves the adoption of domain knowledge to generate features so that it makes the prediction process more effective for well-tailored machine learning models. In addition to the available lung function parameters, we generate new features from a host of anthropometric indices included in the dataset by employing ARTP respiratory equations.