Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.
Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.
Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.
In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e. Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique. The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection. Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering. Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer. So, the proposed system gives a new approach in the field of cancer disease diagnosis. The results obtained by different Hierarchical Clustering Techniques are discussed.
In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease.Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies.This research work incorporates Quantum computing with the clustering algorithm i.e.Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique.The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection.Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering.Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer.So, the proposed system gives a new approach in the field of cancer disease diagnosis.The results obtained by different Hierarchical Clustering Techniques are discussed.
Diagnosis and exact recommendation for a cancer patient is very much sensitive and of greater influencing in cure of this diseases.Traditionally the diagnosis procedure was very time consuming and not so accurate, so here in this respect there is a need of more accurate, fast and easily to understand method / diagnosis tool which gives reliable results.Here we use the shor algorithm, Hce tool and we get accurate and frequent result, when we adopt the approach of quantum computing with the traditional tools of diagnosis.
Quantum computing and diagnostic technologies are two fields that are being integrated to redefine the possibilities of modern medicine, especially the speed and accuracy with which the disease can be identified. In this chapter, the authors are talking about the quantum computing revolution, along with its other applications in advanced algorithms, diagnostic sensors, and machine learning, to change the way we detect and respond to illnesses. Having on advanced quantum concepts, like superposition, entanglement, and/or quantum parallelism, diagnostic computing is able to receive, process, and clear of enormous amounts of data in record time; therefore, it can be able to detect diseases in advance and correctly detect diseases. Diagnostics based on quantum allow empowering the change of healthcare systems, both assessment and identification of specific cancer-related molecules in body fluids and tissues, as well as predicting adverse infectious diseases, including COVID 19. The chapter outlines the use of real-time applications, the prevailing obstacles, and opportunities along with the principles and required technology infrastructure to incorporate the innovative and leading edge technologies into the clinical practices. With the current fast development of these technologies, the perspective of faster, more accurate, and more customized diagnostics has a potential to save countless lives and redefine the health of populations across the globe.
Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.