Enhancing Brain ECVT Analysis of Phantom Position using kNN Algorithm

    Maulana R Ibrahim[1], Muhammad F Ihsan[1], Rizky Maharani[1], Warsito P Taruno[1]

    International Symposium on Biomedical Engineering (ISBE), Jakarta, 2016

    [1] CTECH Labs Edwar Technology

    Corresponding Author Email: maulanarizalibrahim@gmail.com


    Abstract:

    This research implemented nearest neighbor algorithm to classifies the position of phantom after scanned using Brain Electrical Capacitance
    Volume Tomography (ECVT). With
    k-Nearest Neighbors algorithms, we evaluated 496 values of capacitance measurement to find its nearest
    neighbors (highest similarity) and classifies its exact position in the sensor. The performance of this technique reached 93.33% of accuracy.

    Keywords:

    k-nearest neighbor; brain ECVT; supervised learning; phantom prediction

    Source:

    International Symposium on Biomedical Engineering (ISBE), Jakarta, 2016