AI Diabetes Risk Prediction Using Stress, BMI, and Visual Data
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Solution Overview
Problem
Existing diabetes prediction systems lack accurate identification of critical parameters for predicting blood sugar levels, particularly using smart devices and wearable technology, leading to inadequate early diagnosis and treatment.
Innovation Solution
An AI-based method utilizing multiple AI models to monitor physiological parameters, behavioral indicators, and visual representations, including stress levels, BMI, and fat distribution patterns, to predict blood sugar levels and diabetes risk, with adaptive learning and federated learning for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used, then the system is simpler, but the accuracy of diabetes diagnosis is insufficient
Solution Approach 1:
The patent divides the prediction system into multiple specialized AI models, each responsible for processing specific input types (physiological parameters, behavioral indicators, visual representations). This segmentation allows each model to be optimized for its specific task while collectively achieving high overall accuracy, resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent creates a unified AI-based prediction system that handles multiple types of input data (physiological, behavioral, visual) and produces comprehensive diabetes risk assessment. This multi-functional approach increases diagnosis accuracy while the integrated architecture manages complexity through standardized processing pipelines.
2Measurement precision
If multiple critical parameters are monitored, then the prediction accuracy improves, but the data collection and processing complexity increases
Solution Approach 1:
The patent segments the monitoring of multiple critical parameters into distinct categories (physiological parameters, behavioral indicators, visual representations) with dedicated AI models for each. This segmentation makes the complex data collection and processing manageable by treating each parameter type systematically and independently.
Solution Approach 2:
The patent introduces AI models as intermediary components that process raw data from multiple sources and transform them into meaningful predictions. These intermediaries handle the complexity of data processing by automatically extracting relevant features and integrating them into comprehensive risk assessments.
3Reliability
If early diagnosis is implemented, then the prevention of complications improves, but the need for continuous monitoring increases
Solution Approach 1:
The patent implements continuous monitoring of multiple parameters through AI models that operate continuously to detect early signs of diabetes. This continuous action enables early diagnosis while the automated nature of the system minimizes time loss by eliminating manual intervention requirements.
Solution Approach 2:
The AI-based system performs self-service monitoring and analysis, automatically detecting and predicting diabetes risk without requiring continuous manual intervention. The system serves itself by continuously processing data and generating predictions, enabling early diagnosis while reducing the time investment required from users.
Data Source
AI summary
Present disclosure describes techniques for predicting diabetes risk in patients. The techniques include the step of monitoring a plurality of patient-specific characteristics comprising, at least one physiological parameter, one behavioral indicator, and one visual representation of the patient. The method further comprises extracting, using a first artificial intelligence (AI) model, a stress level of the patient based at least on behavioral indicators, historical lifestyle data, and sensor-derived physiological parameters. The method then include extracting, using a second AI model, a body mass index (BMI) or fat distribution patterns based at least on silhouette images and weight of the patient. The method finally includes predicting, using a third AI model, a blood sugar level or diabetes risk score of the patient based on outputs from the first and second AI models and the monitored characteristics.