Performance evaluation of internal quality control rules, EWMA, CUSUM, and the novel machine learning model

Abstract

The present study set out to build a machine learning model to incorporate conventional quality control (QC) rules, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) with random forest (RF) algorithm to achieve better performance and to evaluate the performances the models using computer simulation to aid laboratory professionals in QC procedure planning.

Keywords

• clinical laboratory
• cusum
• ewma
• internal quality control
• machine learning
• risk management
• iç kalite kontrol
• makine öğrenimi
• üaho
• kütop
• klinik laboratuvar
• risk yönetimi