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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 …