Heart Disease Prediction Using Bagging QSVC Algorithm

Quantum computing, harnessing the principles of quantum mechanics, promises dramatic computational leaps. At its core are qubits, the quantum counterparts of classical bits. Unlike binary bits, qubits can exist in a superposition of both 0 and 1 simultaneously, unlocking immense processing power for specific tasks. Quantum machine learning combines this potential with established machine learning algorithms, pushing the boundaries of disease prediction, particularly in the medical field. This study stages an epic showdown between classic machine learning veterans (Logistic Regression, Decision Tree, etc.) and their rising quantum rivals (QSVC, QKNN). Bagging ensembles, combining multiple models, significantly boosted the accuracy of quantum classifiers. We propose a Bagging ensemble model with QSVC, achieving the highest prediction accuracy among all algorithms compared. This study's breakthrough with QSVC opens doors for quantum machine learning to transform early disease detection and pave the way for further advancements across various fields. The power of quantum computing, its application in medical prediction, the effectiveness of bagging ensembles with QSVC, and its superior performance compared to traditional methods. Bagging QSVC was able to predict with accuracy of 91.8% and F1-score of 0.9254.