Quantum Machine Learning-based Lung Cancer Prediction Framework for Healthcare 4.0

Quantum machine learning (QML) is a rising research field that is an intersection of two major domains -quantum computing and machine learning (ML). In recent times, it has been employed to solve complex problems in domains such as image processing, healthcare, drug discovery, natural language processing and finance. The advantages of QML algorithms over traditional ML models include reduced computation resources and the ability to handle and solve complex problems as well as multiple parameters. In this study, we employ QML models for lung cancer prediction in the smart healthcare system domain. We adopted QML approaches, i.e., VQC and Pegasos QSVC, to improve the detection efficiency of lung cancer. We used different performance parameters, such as training accuracy and statistical measures, to evaluate the QML models and also performed a comparative study with the existing baseline models. We analyse that the Pegasos QSVC model outperforms other QML models with a classification accuracy of 85%.