ASD Risk Prediction Model Using Random Forest Feature Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ASD risk prediction models are inefficient and inaccurate due to high labor costs, lengthy evaluation processes, and variability in diagnosis results, necessitating a high-accuracy prediction model that can efficiently process evaluation items and provide reliable data.
Innovation Solution
A method and device for constructing an ASD risk prediction model using a random forest machine learning algorithm, involving data table establishment, characteristic arrangement, marker grouping, and model training to identify best characteristic combinations, which are then used to construct a predictive model for accurate ASD risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation by doctors is used to diagnose ASD, then diagnostic accuracy can be maintained through professional assessment, but labor cost increases significantly and evaluation time is extended
Solution Approach 1:
The patent creates a computational copy of the diagnostic process by training machine learning models on expert evaluation data. The model learns from multiple doctor assessments and reproduces their diagnostic reasoning, enabling automated predictions that mirror professional judgment without requiring actual physician involvement for each case.
Solution Approach 2:
The patent replaces the mechanical system of manual clinical evaluation with an automated computational system. Machine learning algorithms process evaluation item results and generate diagnostic predictions, substituting human cognitive processes with computational operations that are faster and more consistent.
2Measurement precision
If existing ASD risk prediction models include many evaluation items to improve accuracy, then prediction precision may increase, but processing time increases and efficiency decreases
Solution Approach 1:
The patent extracts and identifies the most critical evaluation items from a comprehensive set of assessment parameters. Through feature selection techniques, it isolates the key characteristics that contribute most to diagnostic accuracy, eliminating redundant evaluation items while preserving predictive power.
Solution Approach 2:
The patent segments the diagnostic process into distinct computational stages: data preprocessing, feature extraction, model training, and prediction generation. This segmentation allows parallel processing of different evaluation items and optimizes the computational workflow to improve efficiency without sacrificing accuracy.
3Reliability
If existing ASD risk prediction models use complex evaluation processes to improve accuracy, then prediction reliability may increase, but the error rate increases and results become less accurate
Solution Approach 1:
The patent implements self-service through automated data processing and model validation. The system automatically preprocessed evaluation results, selected optimal features, trained models on diverse datasets, and validated predictions through cross-validation techniques, eliminating manual intervention errors and ensuring consistent processing standards.
Solution Approach 2:
The patent incorporates feedback mechanisms where model predictions are continuously validated against actual diagnostic outcomes. The system learns from prediction errors and adjusts its parameters, creating a closed-loop system that improves reliability while maintaining accuracy through iterative optimization.
Data Source
AI summary
The present disclosure provides a method and device for constructing an autism spectrum disorder (ASD) risk prediction model. The method includes: establishing a first data table and a second data table based on case information of a sample set, obtaining a first grouped table set and a second grouped table set according to a preset characteristic arrangement rule and marker grouping rule, training data based on a random forest machine learning algorithm, and importing test data to obtain a first best characteristic combination and a second characteristic combination; and obtaining a first model based on the first best characteristic combination, stratified sampling of the first data table, and the random forest machine learning algorithm, obtaining a second model based on the second best characteristic combination, stratified sampling of the second data table, and the random forest machine learning algorithm, and performing combination to construct an ASD risk prediction model.