Research Article Open Access

Boosting-BoW Algorithm for Finding Kidney Diseases from Medical Test Reports

Wisam A. Qader1 and Abbas M. Ali2
  • 1 Tishk International University, Iraq
  • 2 Salahaddin University, Iraq
Journal of Computer Science
Volume 15 No. 4, 2019, 558-565

DOI: https://doi.org/10.3844/jcssp.2019.558.565

Submitted On: 27 December 2018 Published On: 26 April 2019

How to Cite: Qader, W. A. & Ali, A. M. (2019). Boosting-BoW Algorithm for Finding Kidney Diseases from Medical Test Reports. Journal of Computer Science, 15(4), 558-565. https://doi.org/10.3844/jcssp.2019.558.565

Abstract

This paper introduces an approach to increase the accuracy rate of classification by employing Bag-of-Words (BoW) as a feature selection method along with machine learning algorithms to obtain a more accurate output. Because of its capability in quickly processing large sets of data and getting accurate results, this approach can be used in medical areas. Different ensemble approaches are generated by different researchers to obtain good results as mentioned in the literature review. In this study a novel algorithm is proposed to analyze medical kidney test reports, using BoW for selecting the features and analyzing them via Boosting four different machine learning classification algorithms like Sequential Minimum Optimization (SMO), k-Nearest Neighbors (k-NN), Random Forests (RF) and Naïve Bayes (NB). With the help of specialists in urology, the proposed algorithm is tested against multiple datasets of different kidney tests. The accuracy of the proposed Boosting algorithms outperforms its counterpart algorithms like SMO, k-NN, RF and NB when they had showen their performances alone.

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Keywords

  • Bag-of-Words
  • Sequential Minimum Optimization
  • k-Nearest Neighbors
  • Random Forests
  • Naïve Bayes
  • Boosting Algorithms