QUANTUM NEURAL NETWORKS FOR DISEASE TREATMENT IDENTIFICATION
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 57-67
AbstractMachine learning is the advanced methodology to solve the issues related to the real world. The problems of the real-world can be solved with the help of medical science, where plenty of varied solutionscan be found for a particular problem. Implementation of the QNN (Quantum Neural Networks) is the best way to solve the problem of identification of diseases. Quantum machine learning is divided into two distinct parts. The first part describes the concept of Quantum data, which is data formed in the natural quantum system or an artificial system. The second method is the Hybrid model which is an advanced version of quantum science machine learning. QML is a better way to analyse the disease relations. The symptoms of the disease can also be analysed using the machine learning model.An advanced level of QNN has been applied here, which is of utmost importance for analysing thesymptoms affecting the person. A chain of prescribed processes has been identified forthe proposed methodology. The accuracy achieved thereafter, in identifying the disease relations using machine learning, was quite high. QCN (Quantum Communication Networks)worked recorded approximately 93% accuracy in identifying the disease symptoms and treatment relations.
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