Decision Stump and StackingC-Based Hybrid Algorithm for Healthcare Data Classification

Sunil Kr. Jha, Parimala Paramasivam, Zhaoqing Pan, Jinwei Wang

Abstract: The healthcare data analytics is a demanding task at present due to enormous amount and diversity of information. Several algorithms were developed to analyze healthcare data with the objective to develop a non-invasive, unbiased and robust prediction system. The present study proposed a hybrid algorithm for healthcare data mining by using the Decision stump (DS), StackingC (SC), and voting methods. Five benchmarked healthcare datasets related to cancer, diabetes, hyperthyroid, lymphography have been selected for the analysis and validation. The hybrid algorithm DS-SC results in 0.31%-30.05% improvement in classification accuracy with DS and SC methods, individually.