Secure agent-based diagnosis and classification using optimal kernel support vector machine Online publication date: Mon, 13-Sep-2021
by Kiran Tangod; Gururaj Kulkarni
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 37, No. 1, 2021
Abstract: Diabetes is a serious complex condition which can affect the entire body. The existing multi-agent-based diabetes diagnosis and classification methods require a number of agents and hence communication between those agents causes time complexity issues. Our method requires only three agents which are user agent, security agent and updation agent. Initially, user agent collects the user symptoms and then encrypts. The encrypted symptoms are then directed to the updation agent. For secure communication two fish-based encryption is used between the user and updation agent. After receiving the encrypted data from the security agent, the updation agent needs to find the diabetes level of user as if it is normal or abnormal. Our proposed technique uses the optimal kernel support vector machine algorithm (OKSVM) with sequential minimal optimisation (SMO) to classify the diabetes level. Based on the optimal kernel, the suggested technique effectively prescribes the drugs for the corresponding user.
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