Machine Learning Ensemble for Disease Prediction Accuracy
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Solution Overview
Problem
Existing predictive modeling via machine learning faces challenges in accurately uncovering causal relationships between animal attributes and diseases, and in effectively displaying improved prediction results to users.
Innovation Solution
A multi-stage machine learning process that combines outputs from different individual models using a combiner model, along with techniques for improved display of simulation results, such as storing simulation results for efficient retrieval and generating user-friendly display elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple individual machine learning models are used to predict diseases, then measurement precision of disease predictions is improved, but device complexity increases due to needing to combine multiple models
Solution Approach 1:
The patent combines multiple individual machine learning models into a single ensemble model that integrates their predictions. The ensemble model processes animal attribute data and generates disease predictions by aggregating outputs from multiple base models, thereby improving prediction accuracy while managing complexity through unified model architecture.
Solution Approach 2:
The ensemble machine learning model serves multiple functions: it performs disease prediction, handles uncertainty quantification, and provides probabilistic outputs for multiple disease types simultaneously. This multi-functional approach allows a single model to replace multiple specialized models while maintaining comprehensive predictive capabilities.
2Reliability
If simulations are run to generate disease predictions, then reliability of predictions is improved, but loss of time increases due to computational requirements
Solution Approach 1:
The patent implements a two-stage approach where a first machine learning model performs rapid preliminary disease prediction and screening. This preliminary action filters obvious cases and provides initial risk assessments before more computationally intensive simulations are applied, thereby reducing overall computation time while maintaining prediction reliability.
Solution Approach 2:
The system applies simulations selectively based on predicted risk levels. For low-risk predictions, simplified models are used; for high-risk cases, full simulations are performed. This partial application of computational resources optimizes the balance between reliability and computation time by avoiding excessive simulation for all cases.
Data Source
AI summary
Techniques for predictive disease identification using simulations improved via machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases; generating disease contraction statistics based on the outputs of the at least one machine learning model; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.