文章基本信息

标题：Boosting-BoW Algorithm for Finding Kidney Diseases from Medical Test Reports
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作者：Qader, Wisam A. ; Ali, Abbas M.
期刊名称：Journal of Computer Science
印刷版ISSN：1549-3636
出版年度：2019
卷号：15
期号：4
页码：558-565
DOI：10.3844/jcssp.2019.558.565
出版社：Science Publications
摘要：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.
关键词：Bag-of-Words; Sequential Minimum Optimization; k-Nearest Neighbors; Random Forests; Naïve Bayes; Boosting Algorithms