Machine Learning Based Image Feature Recognition and Clinical Nursing of Children’s Rheumatoid Arthritis-Related Lung Injury

This work was aimed at investigating image feature recognition and clinical nursing of children’s rheumatoid arthritis- (CRA-) related lung injury under maximum correlation minimum redundancy algorithm of machine learning. In this study, 18 children with CRA in the hospital were selected as the rheumatoid group to explore the nursing method, and 18 healthy children were selected as the control group. The maximum correlation minimum redundancy algorithm of machine learning was compared with the information gain algorithm and the Fisher score algorithm and applied in computed tomography (CT) images of 18 CRA children. The classification accuracy of the algorithm in this study (94.52%) was higher than that of the information gain algorithm (88.64%) and Fisher score algorithm (81.24%). CT alveolitis score (2.35 ± 0.72 points) of children from the rheumatoid group was markedly higher than that of the control group (1.21 ± 0.24 points) (t = 2.147 and P < 0.05 ). The nitric oxide level (14.00 ppb) of children from the rheumatoid group increased greatly compared with the control group (10.00 ppb) ( P < 0.05 ). CRA can cause a decline of lung function in children, while the nitric oxide level exhaled by children can assess the activity of RA. In addition, adopting active nursing methods can help children get better.