Comparative Performance Analysis of Quantum Algorithm with Machine learning Algorithms on Diabetes Mellitus

Abstract

This paper shows the distinction between Quantum and Classical Machine Learning techniques appeal to a diabetic mellitus dataset. Diabetes mellitus is like series of diseases that affect the body and deplete blood sugar levels (insulin). In our bodies, glucose is an important source of energy for powering mitochondria, the cells' function that make muscles and tissues strong. We use many machine learning classifiers such as Support Vector Machine, Kernel Principal Component Analysis, Bayesian Network and Decision Tree etc. These models are able predict a certain amount of data, while in case of large data there is significant amount of error and low accuracy rate, A new method known as quantum machine learning (QML) is nothing but the collaboration of machine learning in the way of quantum algorithms the term used for machine learning algorithms executed in a quantum computer for analysis of classical data is known as quantum-enhanced machine learning. In this method we are predicting diabetes mellitus using quantum machine learning algorithms which able to handle the huge data with high accuracy rate of 97% and F1-Score of 68%.Our study having implemented models of Predicted & Enhanced models.