An efficient disease prediction and classification using feature reduction based imputation technique

UshaRani, Yelipe; Sammulal, P.

doi:10.1109/icemis.2016.7745363

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…In [18], missing medical data was recovered using k nearest neighbour (kNN) imputation method along with three classifiers: naïve Bayes, decision trees and random forest. In [30], missing medical data was recovered and classified using imputation based on Class Based Clustering (IMCBC). In [19], authors proposed imputation based on class-based clustering (IM-CBC) to recover missing medical data and classified data using Class-Based-Clustering Classifier (CBCC-IM).…”

Section: A Related Workmentioning

confidence: 99%

Classification Analysis of Tensor-Based Recovered Missing EEG Data

Akmal

Zubair²,

Alquhayz

2021

IEEE Access

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This paper discusses impact of recovering missing Electroencephalography (EEG) data on classification accuracy of hand movements using tensor-based methods. Improvement in classification accuracy is important for efficient performance of prosthesis. Classification accuracy relies on quality of the observed data. In practice, observed data is usually incomplete because of disconnection of electrodes and other artefacts, which negatively affect the classification accuracy. In this paper, we employ tensor-based imputation methods (canonical/polyadiac decomposition (CPD), weighted optimization version of CPD (CP-WOPT and Nonnegative Matrix Factorization (NMF)) to recover missing data in EEG signals and apply various classifiers to classify hand movements on recovered data. In particular, structured missing data was considered because that is how data gets missed in real-life data acquisition. Percentage of missing data was changed from 10% to 50%. Classifiers are applied on complete, missing and recovered data explicitly to test the performance of our framework. Results show that mean classification accuracy on complete data, missing data and recovered data was 71%, 53% and 64% respectively which shows applicability of our framework.

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Section: A Related Workmentioning

confidence: 99%