Decision Tree for Disease Prediction Using Physiological Data
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Solution Overview
Problem
Current methods for disease prediction rely heavily on medical professionals' experience, often leading to delayed treatment due to misdiagnosis, and there is a need for a more efficient mechanism to classify physiological measurement data for accurate disease identification.
Innovation Solution
A method for establishing a decision tree using physiological measurement data, involving classification, calculation of cutting points, branching, and pruning, utilizing the Gini coefficient and Akaike Information Criterion to improve disease prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If doctors rely on their experience to determine disease, then diagnostic flexibility is maintained, but misdiagnosis rate increases and treatment is delayed
Solution Approach 1:
The system performs preliminary classification of physiological measurement data into multiple categories before final diagnosis, pre-organizing information to speed up the diagnostic process while maintaining accuracy through structured analysis
Solution Approach 2:
The diagnostic process is segmented into distinct stages: data classification into multiple categories, cutting point calculation for each category, and hierarchical branching through terminal branches, allowing parallel processing that reduces time loss while maintaining comprehensive analysis
2Reliability
If artificial intelligence is introduced to assist disease recognition, then misdiagnosis rate decreases, but system complexity increases
Solution Approach 1:
The AI system is segmented into modular components: data classification module, cutting point calculation module, and terminal branch determination module, each handling specific tasks independently to reduce overall system complexity while maintaining high recognition accuracy
Solution Approach 2:
The system performs preliminary classification of physiological data into multiple categories before detailed analysis, pre-organizing information to simplify subsequent processing steps and reduce the complexity of the overall diagnostic system
3Measurement precision
If complete medical records are imported using FHIR format, then diagnostic accuracy improves, but data processing complexity increases
Solution Approach 1:
Complete medical records in FHIR format are segmented and classified into multiple physiological measurement categories, with each category processed independently through its own cutting point calculation and terminal branch determination, reducing the complexity of handling large volumes of data while maintaining complete information for accurate diagnosis
Data Source
AI summary
A method for establishing a decision tree for disease prediction is provided. The method receives a plurality of physiological measurement data corresponding to different diseases. The method classifies the physiological measurement data corresponding to the purpose. The method calculates at least one cutting point of the physiological measurement data. The method branches the decision tree corresponding to the at least one cutting point. The method prunes the decision tree to complete the establishment of the decision tree. The present invention can assist doctors in medical diagnosis, give preventive medication in advance based on the prediction results, and calculate the data of each terminal branch of the decision tree to obtain the probability of a single disease, which can improve the accuracy of more disease predictions.