Machine learning prediction models for the popularization and dissemination of medical science popularization videos.

Objective: To summarize the current production and release trends of medical science popularization videos, analyze the effect of non-medical factors on their spread, and develop dissemination-prediction models using machine learning (ML) algorithms. Methods: We identified a sample of medical science popularization videos on TikTok ( Results: In the quantitative analysis of the 4 outcomes, we identified significant disparities among different videos, "Thumb-Up" with a range from 0 to 2.72 million, "Collection" with 1 to 1.36 million, "Share" with 1 to 898 thousand, and "Comment" with 0 and 200 thousand. Subsequently, four best-performing models were ultimately confirmed through internal and external validation, all of which were RF models, including "Thumb-Up" (AUC = 0.8802), "Collection" (AUC = 0.7685), "Share" (AUC = 0.7872), "Comment" (AUC = 0.8077). Weight analysis identified the video duration, video description length, and shooting in department office emerged as the most three crucial parameters across all four models. Conclusion: This study demonstrates the significant impact of non-medical factors on the dissemination of medical science popularization videos, and shows that prediction models base on these factors can effectively forecast video spread and popularity. These findings may contribute to enhancing These findings, thereby advancing health education and strengthen public health literacy.