A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making

Abstract

From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but determining how to effectively deal with the multi-dimensionality and high volume of generated data obtained from Medical IoT-based healthcare systems is an issue of increasing importance in IoT healthcare data exploration and management. A novel classifier or predicator equipped with a good feature selection function contributes effectively to classification and prediction performance. This paper proposes a novel bagging C4.5 algorithm based on wrapper feature selection, for the purpose of supporting wise clinical decision-making in the medical and healthcare fields. In particular, the new proposed sampling method, S-C4.5-SMOTE, is not only able to overcome the problem of data distortion, but also improves overall system performance because its mechanism aims at effectively reducing the data size without distortion, by keeping datasets balanced and technically smooth. This achievement directly supports the Wrapper method of effective feature selection without the need to consider the problem of huge amounts of data; this is a novel innovation in this work.

Keywords: Bagging algorithm; C4.5 decision tree; Ensemble learning; Sampling method; Wrapper feature selection.