Discriminator Sequence Selection for Data Analysis Accuracy
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Solution Overview
Problem
Conventional data analysis methods fail to properly identify determinative indicators for diagnosis and prediction, leading to misdiagnosis and inefficiencies in selecting conclusions based on empirical data.
Innovation Solution
A method involving the selection and ranking of discriminators from threshold and non-threshold indicators, generated from training data sets, to accurately select conclusions from test data sets, optimizing sensitivity and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used to draw conclusions from empirical data, then the analysis process is simple, but the accuracy of identifying determinative indicators deteriorates leading to misdiagnosis
Solution Approach 1:
The patent segments the data analysis process into distinct phases: generating multiple candidate conclusions from empirical data, ranking these conclusions based on their determinative value, and selecting the highest-ranked conclusion. This segmentation allows the system to systematically evaluate multiple potential indicators rather than relying on conventional single-step analysis, thereby improving identification accuracy while maintaining manageable process complexity
Solution Approach 2:
The patent applies preliminary action by pre-ranking and pre-selecting determinative indicators before the actual diagnostic conclusion is drawn. The system generates and ranks multiple candidate conclusions in advance, identifying which indicators are most determinative before making the final diagnostic decision. This preliminary evaluation ensures that the most relevant indicators are prioritized, improving measurement precision in the final diagnosis
2Reliability
If conventional data analysis methods are used to select conclusions, then the method is easy to operate, but the ability to properly utilize relevant indicators deteriorates
Solution Approach 1:
The patent implements feedback by using the ranking mechanism to continuously evaluate and prioritize indicators based on their relevance to the conclusion. The system provides feedback on which indicators are most determinative, allowing the analysis to iteratively refine its selection of relevant indicators. This feedback loop ensures that the most pertinent data is utilized in conclusion selection, enhancing reliability while the automated nature of the process maintains ease of operation
Solution Approach 2:
The patent applies parameter changes by transforming the raw empirical data into ranked conclusions with associated determinative values. The system changes the parameters of data utilization by assigning ranks and priority levels to different indicators, allowing the most relevant parameters (indicators) to be weighted more heavily in the final conclusion selection, thereby improving reliability without complicating the operational process
Data Source
AI summary
A method for data analysis according to various aspects of the present invention generally includes selecting a conclusion from a plurality of conclusions for one or more test data sets by generating discriminators from one or more threshold indicators associated with the conclusion, selecting a portion of the discriminators, ranking the discriminators in a sequence, and applying the sequence of discriminators to one or more test data sets to select the conclusion.