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Lecture notes in electrical engineering

Enhancing Diabetes Prediction with Hybrid Traditional and Quantum Machine Learning Models

Gowripushpa Geddam, N. Ramadevi, P. Srilatha, Vunnava Dinesh Babu, D. Chandra Mouli, A. Lakshmana Rao

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-981-96-9720-5_22

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Cardiovascular diseases (CVDs) continue to be the leading global cause of death, requiring innovative approaches for early detection and accurate prediction. This study explores the transformative potential of quantum-enhanced machine learning (queml) models in cardiovascular disease prediction, addressing the limitations of conventional diagnostic approaches. Using extensive datasets including the Kaggle Heart Disease Dataset of 1,190 patient records and the Cleveland/State Registry datasets, the quantum-enhanced models show superior performance over traditional machine learning approaches, with QCVNET achieving 91.73% accuracy and queml showing a 0.6% improvement in accuracy, while reducing training time from 862.5 µs to 670 µs per cycle. Key physiological parameters including diabetes indicators, LDL cholesterol, HDL cholesterol, and triglycerides were incorporated as predictive features. The IQCNN framework achieved 91% classification accuracy at a learning rate of 70%, significantly outperforming the Bi-LSTM and CNN approaches. Comparative analysis revealed exceptional AUC scores of 96.43% and 97.78% for the hybrid models, establishing quantum-enhanced machine learning as a highly effective solution for cardiovascular risk assessment and early detection systems.
Abstract The paper focused on the implementation of Quantum machine learning and artificial intelligence techniques in diagnosing diseases, specifically focusing on diabetes. The paper proposed an ensemble approach that combined classical algorithms with quantum processing unit (QPU)--based algorithms to improve the performance of a model. The diabetes dataset used in the study is obtained from the (Centre for Disease Control and Prevention (CDC) repository, and the goal is to classify patients as either diabetic or non-diabetic. The ensemble algorithms examined in the study include Voting classifier, Adaboost, Xgboost, Catboost, and QPU-based Qboost. While Qboost demonstrates some quantum speedup, its performance is not satisfactory. Therefore, the proposed hybrid model is developed to enhance the performance metrics. The hybrid model achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.89, 0.85, 0.95, 0.90, and 0.96, respectively, on the diabetes dataset. In comparison, the top-performing Adaboost algorithm achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.94, 0.91, 0.98, 0.94, and 0.98, respectively. The paper concludes that while quantum computing (QC) significantly improves computation speed, it comes at a slight cost of a 5 \% decrease in classification metrics and 0.186 in the AUC score. Additionally, the study suggests that further development of Quantum computing hardware will enhance overall performance metrics.