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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      07 April 2023, Volume 38 Issue 2   
    Article

    MODERN DETECTION METHODOLOGY FOR LIVER CONTINUAL DISEASES WITH HEPATITIS AND CIRRHOSIS AVOIDANCE BY ESTIMATING THE EUCLIDEAN DISTANCE USING MACHINE LEARNING ALGORITHM
    Dr. Ashaq Hussain Bhat1, Syed Musthafa2, B.Latha3, T Yogameera4, Dr D.Shanthi5, Fathima H6
    Journal of Data Acquisition and Processing, 2023, 38 (2): 2458-2469 . 

    Abstract

    Machine Learning (ML) permits the user to feed a computer based algorithm an enormous quantity of data and encompass the computer to analyze and make decisions and data-driven recommendations based on the input data. ML software parses this data and then “learns” from it by applying patterns from which it is able to make predictions. In the healthcare based application ML can be used to help and detect disease. In recent years, liver oriented disease has materialized as one of the normal occurring disease worldwide. Various algorithms are used to predict the liver disease. A correlation distance metric and nearest rule based k-nearest neighbor approach is used as an effective way to predict the liver disease. Intelligent classification algorithms employed on liver patient dataset are linear discriminant analysis (LDA), diagonal linear discriminated analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), least squares support vector machine (LSSVM) and k-nearest neighbor (KNN) based approaches are some of the techniques used for detecting liver disease. It is observed that KNN based approaches are superior to all classifiers in terms of attained accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive (NPV) value rates. Liver diseases are usually detected in the advanced stage. Hence, mortality rate is high in case of liver patients. Early diagnosis of liver diseases can definitely improve this. Diagnosis of normal and diseased liver is done using clinical data which is derived through blood tests. In this paper an attempt is made to make demarcation between the normal and diseased liver using K-nearest-neighbor (KNN).

    Keyword

    Liver Disease, KNN, ML, Medical Science, Linear analysis


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