Classification Model on Big Data in Medical Diagnosis Based on Semi-Supervised Learning

Abstract

Abstract Big data in medical diagnosis can provide abundant value for clinical diagnosis, decision support and many other applications, but obtaining a large number of labeled medical data will take a lot of time and manpower. In this paper, a classification model based on semi-supervised learning algorithm using both labeled and unlabeled data is proposed to process big data in medical diagnosis, which includes structured, semi-structured and unstructured data. For the medical laboratory data, this paper proposes a self-training algorithm based on repeated labeling strategy to solve the problem that mislabeled samples weaken the performance of classifiers. Aiming at medical record data, this paper extracts features with high correlation of classification results based on domain expert knowledge base first, and then chooses the unlabeled medical record data with the highest confidence to expand the training set and optimizes the performance of the classifiers of tri-training algorithm, which uses supervised learning algorithm to train three basic classifiers. The experimental results show that the proposed medical diagnosis data classification model based on semi-supervised learning algorithm has good performance.