Medical Diagnosis Support System for Lung Function Prediction

The system integrates multiple diagnostic functions into a single platform, including lung function prediction, lifestyle habit analysis, and visual feedback generation. The trained identifier serves as a universal computational model that can process various input parameters (age, smoking habits, occupational exposure) and generate comprehensive diagnostic outputs, thereby improving reliability without proportionally increasing complexity.

The patent introduces visual intermediaries such as X-ray images, spirometry graphs, and diagnostic charts as mediators between the complex computational processes and the end user. These visual representations simplify the interpretation of prediction results, making the complex system more usable while maintaining diagnostic accuracy.

The system performs preliminary processing by collecting and organizing lifestyle habit information (smoking habits, occupational exposure, exercise routines) before the actual prediction process. The trained identifier is pre-trained on comprehensive datasets, enabling it to process new inputs efficiently without requiring intensive real-time computation, thus balancing predictive accuracy with data processing requirements.

The patent uses visual copies and representations of lung function data (X-ray images, spirometry curves) that can be generated and stored without requiring the original complex computational data. These visual copies serve as efficient representations that maintain diagnostic value while reducing data processing and storage requirements.