Disease detection frameworks having blend of distinct fields of concerned research are the need of hour as the domain is very challenging and essential for the human race. Its expanding every day and its impact is much bigger than the earlier era. Quality of service(QoS) is an important consideration for designing sophisticated futuristic healthcare systems. One such quality parameter of reliability is consolidated in this disease detection framework incorporating information-centric networking of medical knowledge-base and protein/gene knowledge-base. The proposed framework is designed and implemented keeping in view of research progress of two highly interrelated research areas which are often not used together but can be combined to formulate an efficient disease detection system. Machine learning and deep learning is also incorporated to improve the quality of service parameter in terms of accuracy and reliability of the system. Various ML configurations formed a test bed for the accuracy check at different system settings indicating a perfect mix for a specific detection requirement. Results indicate promising detection rate of 71% combined accuracy and a remarkable 97% accuracy rate for specific single class detection along with the improvement in reliability factor.

ICN, Disease detection, Drug discovery, Machine Learning, Quality of Service

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