Genetic Algorithm Optimizes Injury Prediction Model Training
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Solution Overview
Problem
Existing systems for predicting injury levels in vehicle passengers during traffic accidents face challenges in optimizing machine learning model training and structure discovery, requiring recursive testing and configuration of data for enhanced prediction performance.
Innovation Solution
An apparatus and method utilizing a genetic algorithm to select and train input data for a machine learning model, determining its structure, and predicting injury levels by communicating with external databases like NASS/CDS, incorporating models such as decision trees, random forests, or support vector machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recursive testing is used to optimize machine learning model training and structure, then prediction performance is improved, but system complexity and time consumption increase
Solution Approach 1:
The genetic algorithm enables the system to automatically optimize its own training parameters and structure through self-directed evolution. The algorithm autonomously performs parameter tuning, feature selection, and model architecture optimization without requiring external recursive testing, allowing the system to self-improve prediction accuracy while reducing operational complexity
Solution Approach 2:
The genetic algorithm performs preliminary optimization of training parameters, feature sets, and model structures before actual prediction tasks. By pre-evolving optimal configurations through simulated selection and crossover operations, the system prepares optimized models in advance, eliminating the need for complex recursive testing during deployment while maintaining high prediction accuracy
2Measurement precision
If recursive testing is used to discover machine learning model structure, then prediction performance is improved, but training time increases
Solution Approach 1:
The genetic algorithm implements periodic evolution cycles with structured phases of selection, crossover, and mutation. Instead of continuous recursive testing, the system performs discrete generations of model optimization where each generation systematically explores different model structures and parameters, achieving convergence to optimal configurations in predetermined time intervals rather than through endless recursive search
Solution Approach 2:
The patent replaces the mechanical iterative process of recursive testing with a bio-inspired genetic algorithm that simulates natural evolution. This substitution uses probabilistic selection, crossover, and mutation operations to explore model spaces more efficiently than systematic recursive testing, reducing training time while maintaining the ability to discover high-performance model structures
3Measurement precision
If comprehensive data configuration is used for machine learning model training, then prediction performance is improved, but data processing complexity increases
Solution Approach 1:
The genetic algorithm extracts and selects only the most relevant features and data configurations from the comprehensive dataset through its selection mechanism. By identifying and isolating the critical subset of training data that contributes most to prediction accuracy, the system eliminates unnecessary data processing complexity while maintaining high predictive performance through focused training on essential features
Data Source
AI summary
An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.