Hybrid Label Noise Correction Algorithm For Medical Auxiliary Diagnosis

Abstract

In the context of the continuous development of Internet of Things (IoT) technology and Machine learning (ML) technology, its application in the medical field is becoming more and more extensive. However, with a dramatic increase in medical data obtained from the IoT-based medical auxiliary diagnosis system, the impact of label noise problems is also increasing. When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the labels of some patients may adversely affect the performance of the algorithm. For example, due to ambiguous patient conditions or poor reliability of diagnostic criteria, even clinical experts may lack confidence in making medical diagnoses for some patients. As a result, some samples used in algorithm training may be mislabeled, which adversely affects the performance of the algorithm. In this paper, we study a classification problem of sample labels with random damage. We propose a new hybrid label noise correction model that generalizes many learning problems, including supervised, unsupervised and semi-supervised learning. This hybrid model can withstand the negative effects of random noise and various non-random label noise. Extensive experimental results using real-world datasets from UCI machine learning repository are provided, the empirical study shows that our approach successfully improves data quality in many cases, in terms of classification accuracy, over existing label noise correction methods.