用量子启发方法提升糖尿病风险预测准确率与效率
Quantum-Inspired Stacked Integrated Concept Graph Model (QISICGM) for Diabetes Risk Prediction
- 融合量子启发特征映射与多模型堆叠，构建自优化概念图
- 在2768样本上实现0.8933的F1分数和0.8699的AUC
- 代码开源，适合医疗AI研发与可解释性研究者使用
量子启发堆叠集成概念图模型（QISICGM）是一种创新的机器学习框架，通过量子启发技术实现高精度、高效能的糖尿病风险预测。基于增强后的PIMA Indians Diabetes数据集（共2768样本，其中正例1949个，含2000个合成样本以缓解类别不平衡），该模型结合自改进概念图与随机森林（RF）、极端梯度提升（ET）、Transformer、卷积神经网络（CNN）及前馈神经网络（FFNN）的堆叠集成。实验结果表明，其交叉验证外样本（OOF）F1得分为0.8933，AUC达0.8699，显著优于传统方法。量子启发机制如相位特征映射与邻域序列建模增强了特征表达能力，支持每秒8.5行的CPU高效推理。本文详述架构设计、理论基础、代码实现及可视化分析，开源版本（v1.0.0）已发布于https://github.com/keninayoung/QISICGM，具备成为糖尿病等临床辅助分诊基准的潜力。研究强调通过校准、可解释性与开源可复现性实现可信AI。
原文摘要 · Abstract (English)
The Quantum-Inspired Stacked Integrated Concept Graph Model (QISICGM) is an innovative machine learning framework that harnesses quantum-inspired techniques to predict diabetes risk with exceptional accuracy and efficiency. Utilizing the PIMA Indians Diabetes dataset augmented with 2,000 synthetic samples to mitigate class imbalance (total: 2,768 samples, 1,949 positives), QISICGM integrates a self-improving concept graph with a stacked ensemble comprising Random Forests (RF), Extra Trees (ET), transformers, convolutional neural networks (CNNs), and feed-forward neural networks (FFNNs). This approach achieves an out-of-fold (OOF) F1 score of 0.8933 and an AUC of 0.8699, outperforming traditional methods. Quantum inspired elements, such as phase feature mapping and neighborhood sequence modeling, enrich feature representations, enabling CPU-efficient inference at 8.5 rows per second. This paper presents a detailed architecture, theoretical foundations, code insights, and performance evaluations, including visualizations from the outputs subfolder. The open-source implementation (v1.0.0) is available at https://github.com/keninayoung/QISICGM, positioning QISICGM as a potential benchmark for AI-assisted clinical triage in diabetes and beyond. Ultimately, this work emphasizes trustworthy AI through calibration, interpretability, and open-source reproducibility.
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