Multimodal health data fusion and analysis based on digital twins

This research suggests a structured approach for analyzing health data by developing a digital twin model and combining various types of health data. Initially, the raw data was cleaned, filled in missing values, and standardized for data consistency and quality; subsequently, fusion algorithms like weighted average, feature connection, and principal component analysis (PCA) were developed to enhance data expressiveness and information content effectively. By evaluating the data fusion effect, the improvement in indicators such as information gain, data correlation, and prediction accuracy was verified. In terms of health data analysis, statistical analysis, regression analysis, cluster analysis and classification algorithms were applied to conduct in-depth research on the fused data, and cardiovascular disease and diabetes risk assessment were conducted through the Framingham Heart Study data set, demonstrating the effectiveness of the proposed method. Practical application effect. The analysis results show that multi-modal data fusion significantly improves the prediction performance of the model and performs well on multiple indicators. The research results provide a solid theoretical foundation and practical guidance for the intelligent analysis and application of health data.