In semisupervised learning, particularly in dealing with health big data classification problems, optimizing the performance of classifiers has always been a challenge. Accordingly, this study explores an optimization algorithm based on collaborative training to better handle health big data. First, the tri-training and decision tree classification models were selected for comparison. The average classification accuracy of the tri-training classification model was 4.20% higher than that of the decision tree classification model. Subsequently, the standard tri-training classifier was compared with these two classifiers. The classification accuracy of the standard tri-training classifier increased by 3.88% and 4.33%, respectively, compared with the aforementioned two classifiers. Finally, under the condition of 10% labeled samples, the performance of the collaborative training optimization algorithm was verified under three different basis classifiers. The results of this study demonstrate the effectiveness of optimization algorithms based on collaborative training in dealing with health big data classification problems. By improving the performance of the classifier, health big data can be predicted and analyzed more accurately, thereby improving the accuracy and efficiency of medical decision-making. Meanwhile, the application of this optimization algorithm also provides new research directions for other semisupervised learning problems.

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