Multi-level Machine Learning Algorithm for Cardiac Fitness Assessment

The patent segments the complex assessment task into multiple independent machine learning models, each specialized in analyzing specific types of medical measurements (e.g., echocardiography, ECG, CT scans). Each model processes a particular data modality and outputs a diagnostic metric, which are then integrated to form a comprehensive cardiovascular fitness assessment. This segmentation reduces the difficulty by breaking down the complex problem into manageable specialized tasks.

The patent creates a universal multi-level processing algorithm framework that can handle diverse medical measurement types through a common architecture. The system uses a standardized interface and integration layer that accepts multiple input types (imaging, physiological signals, laboratory values) and produces unified diagnostic outputs. This multi-functionality allows the system to comprehensively assess cardiovascular fitness across different disease conditions using a single platform.

The patent divides the machine learning processing system into multiple specialized models, each trained on specific types of medical data. Instead of using one complex monolithic model, the system employs several smaller models that each excel at processing their designated data type (e.g., one model for echocardiography images, another for ECG signals). This segmentation maintains high accuracy for each data type while reducing the overall system complexity through modular design.

The patent introduces an intermediary integration layer that combines the outputs of multiple specialized machine learning models. This intermediary component receives diagnostic metrics from various models, standardizes their formats, and integrates them into a unified cardiovascular fitness assessment. The intermediary layer simplifies the complexity by providing a standardized interface between diverse models and the final assessment output.

The patent segments the computational workload across multiple specialized machine learning models, each optimized for specific data types and disease detections. This allows parallel processing of different measurement types, improving detection accuracy through comprehensive analysis while distributing computational resources efficiently. Each model can be trained and executed independently, optimizing resource utilization.

The patent implements preliminary processing steps that prepare and pre-process medical measurement data before it enters the main multi-level processing algorithm. This includes data normalization, feature extraction, and preliminary filtering that reduce the complexity and size of input data. By performing these preliminary actions, the system reduces the computational resources required by the subsequent diagnostic models while maintaining or improving detection accuracy.