Method and an apparatus for detecting a level of cardiovascular disease
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Classification of medical scan data is hampered by an overabundance of potential parameters, making it difficult for models to converge sufficiently for accurate diagnostics or detection of cardiovascular diseases.
Innovation Solution
An apparatus and method utilizing a processor and memory to receive voltage-time data, generate feature vectors through a feature model, input them into a cardiovascular classification model, and generate disease indications, specifically for myocarditis, enhancing model convergence and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all potential parameters from medical scan data are used for classification, then the completeness of diagnostic information is improved, but the model convergence difficulty increases and computational resources are wasted
Solution Approach 1:
The patent extracts only the most relevant features from the abundant medical scan data parameters. The system identifies and extracts key features that are most predictive of cardiovascular disease and myocarditis, discarding redundant parameters. This extraction process reduces the dimensionality of input data while preserving diagnostic accuracy, enabling model convergence without losing critical diagnostic information.
Solution Approach 2:
The patent segments the classification task into multiple stages: first extracting relevant features from raw data, then feeding these features into a classification model. This segmentation separates the feature extraction process from the classification process, making the overall system more manageable and improving convergence by breaking down the complex task of processing all parameters simultaneously.
2Measurement precision
If more parameters are processed to improve diagnostic accuracy, then the detection precision is improved, but the computational resource consumption increases
Solution Approach 1:
The system extracts only the essential features needed for accurate cardiovascular disease and myocarditis detection, rather than processing all available parameters. This selective extraction maintains high detection accuracy by focusing on the most diagnostic features while significantly reducing computational resource consumption and energy usage.
3Reliability
If a comprehensive set of parameters is used for classification, then the reliability of diagnosis is improved, but the time required for processing increases
Solution Approach 1:
The patent extracts the most time-sensitive and diagnostically critical features from medical scan data, enabling rapid processing that maintains high diagnostic reliability. By focusing on key features rather than processing all parameters comprehensively, the system achieves reliable diagnosis in reduced time.
Solution Approach 2:
The system performs preliminary feature extraction and filtering before the main classification process. This preliminary action prepares the data in advance by identifying and isolating the most relevant features, so that the subsequent classification can proceed quickly with pre-processed, high-value data, reducing overall processing time while maintaining reliability.
Data Source
AI summary
An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.