A Fast Feature Selection Method Based on Coefficient of Variation for Diabetics Prediction Using Machine Learning

Abstract

Diabetes has become a prevalent metabolic disease nowadays, affecting patients of all age groups and large populations around the world. Early detection would facilitate early treatment that helps the prognosis. In the literature of computational intelligence and medical care communities, different techniques have been proposed in predicting diabetes based on the historical records of related symptoms. The researchers share a common goal of improving the accuracy of a diabetes prediction model. In addition to the model induction algorithms, feature selection is a significant approach in retaining only the relevant attributes for the sake of building a quality prediction model later. In this article, a novel and simple feature selection criterion called Coefficient of Variation (CV) is proposed as a filter-based feature selection scheme. By following the CV method, attributes that have a data dispersion too low are disqualified from the model construction process. Thereby the attributes which are factors leading to poor model accuracy are discarded. The computation of CV is simple, hence enabling an efficient feature selection process. Computer simulation experiments by using the Prima Indian diabetes dataset is used to compare the performance of CV with other traditional feature selection methods. Superior results by CV are observed.