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International Conference on ISMAC in Computational Vision and Bio-Engineering

ISMAC 2018: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) pp 31–40Cite as

Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease

Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB,volume 30)

Abstract

Vector borne diseases like malaria fever is one of the most elevating issues in medical domain. Accurate identification of a patient from the given set of samples and classification becomes one of the challenging task when dealing with imbalanced datasets. Many conventional machine learning and data mining algorithms are shows poor performance to classify skewed distributed data because they are trained very well with the majority class samples only. Proposing an ensemble method called majority voting defined with a set of machine learning algorithms namely decision tree—C4.5, Naive Bayesian and K-Nearest Neighbor (KNN) classifiers. Classification of samples can be done based on the majority voting of classifiers. Experiment results stating that voting ensemble method shows classification accuracy of 95.2% on imbalanced malaria disease data whereas dealing with balanced malaria disease data voting ensembler shows 92.1% of accuracy. Consequently voting shows 100% classification report on precision, Recall and F1-Score on imbalanced malaria disease data sets whereas on balanced malaria disease data voting shows 96% of Precision, Recall and F1-Score metrics.

Keywords

  • Malaria disease
  • Balanced data
  • Imbalanced data
  • Voting ensembler

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Authors and Affiliations

  1. Department of Computer Science & Engineering, K L E F, Vaddeswaram, Guntur, India

    T. Sajana & M. R. Narasingarao

Authors
  1. T. Sajana
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  2. M. R. Narasingarao
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Corresponding author

Correspondence to T. Sajana .

Editor information

Editors and Affiliations

  1. SCAD Institute of Technology, Palladam, India

    Durai Pandian

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  3. School of Computer and Security Science, Edith Cowan University, Joondalup, WA, Australia

    Zubair Baig

  4. Wenzhou Medical University, Wenzhou, China

    Fuqian Shi

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Sajana, T., Narasingarao, M.R. (2019). Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-00665-5_4

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