PIMA Diabetes Prediction Using Machine Learning and Quantum Machine Learning Techniques

Quantum Machine Learning (QML) which is the integration of Quantum Mechanics (QM) and Machine Learning (ML), comes with immense computational capacity due to principles of superposition and entanglement. The PIMA diabetes dataset is used in this research as a test bed for comparing the results obtained from ML and QML approaches. The assessment shows that Support Vector Machine (SVM) has better results compared to Logistic Regression (LR) with an accuracy of 0.76. On the other hand, among QML algorithms; the Quantum Support Vector Classifier (QSVC) has a higher accuracy than the Variational Quantum Classifier (VQC) with 0.74. The findings of the given research imply that the use of QML methods may be valuable for further developments in diabetes prediction in the future.