Quality prediction through machine learning for the inspection and manufacturing process of blood glucose test strips

Abstract

Although machine learning for quality prediction of manufacturing processes has attracted attention in the literature, there is a significant lack of case studies from industry, especially in medical sector. This paper proposes a data-driven approach to infer the batch quality of blood glucose test strips. Once the low quality of work in process is detected, unnecessary process waste could be eliminated. Starting from data pre-processing, which consists of Synthetic Minority Over-sampling TEchnique and Random Over-Sampling Example, this project tries to balance the ill-distributed data first. Followed by machine learning aims to classify and predict the quality of blood glucose test strips. Different models are evaluated by the Receiver Operating Characteristic curve and Area Under Curve. Computational results show that the decision tree and random forest after SMOTE perform better than the counterpart of ROSE method. Ensemble learning, such as random forest, out-wins base learner decision tree. To sum up, random forest with SMOTE is the suggested model for accurately predicting the quality of blood glucose test strips. There is a 30% improvement in error rate under random forest and SMOTE for NG class that could be of top concern for prognosis. Several factors, including the direction of applying test reagent onto test strips and the position where the strips are located, that affect quality of test strips have been identified. Explanations in terms of inspection and manufacturing are discussed subsequently. Finally, the prognosis of quality can be attained through big data and statistical machine learning.

Keywords

• quality prediction
• imbalanced data
• over-sampling
• decision trees
• ensemble learning