Analysis of Ensembled Learning using Classical and Quantum Boosting Approaches for Diabetes Mellitus Prediction

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.