Preprocessing unbalanced data using weighted support vector machines for prediction of heart disease in children

Tavares, Thiago Ribeiro; Oliveira, Adriano L. I.; Cabral, George G.; Mattos, Sandra S.; Grigorio, Renata

doi:10.1109/ijcnn.2013.6706947

Cited by 9 publications

(8 citation statements)

References 28 publications

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“…However, among these primary studies, there is sufficient evidence that SLR studies on data preprocessing are lacking, as indicated by the fact that only 2% of the primary studies considered in this study followed SLR guidelines. This finding [136] N P N Y 1.5 [105] N N N Y 1.0 [86] N Y N Y 2.0 [137] N N N Y 1.0 [106] N P N Y 1.5 [83] N Y N Y 2.0 [138] N P N Y 1.5 [139] N P N Y 1.5 [140] N N N Y 1.0 [68] N Y N Y 2.0 [141] N P N Y 1.5 [142] N P N Y 1.5 [67] N P N Y 1.5 [90] N P N Y 1.5 [50] N P N Y 1.5 [53] N N N Y 1.0 [52] N P N Y 1.5 [47] N P N Y 1.5 [48] N N N Y 1.0 [143] N P N Y 1.5 [117] N Y N Y 2.0 [144] N N N Y 1.0 [145] N P N Y 1.5 [73] Y Y Y Y 4.0 [146] N P N Y 1.5 [147] N P N Y 1.5 [148] N N N Y 1.0 [149] N P N Y 1.5 [150] N P N Y 1.5 [60] N P N Y 1.5 [151] N N N Y 1.0 [152] N P N Y 1.5 [153] N N N Y 1.0 [154] N N N Y 1.0 [155] N N N Y 1.0 [156] N N N Y 1.0 [157] N N N Y 1.0 [158] N P N Y 1.5 [159] N N N Y 1.0 [160] N N N Y 1.0 [161] N N N Y 1.0 [162] N N N Y 1.0…”

Section: What Are the Limitations Of Current Research?mentioning

confidence: 99%

Systematic literature review of preprocessing techniques for imbalanced data

Felix

Lee

2019

IET softw.

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Data preprocessing remains an important step in machine learning studies. This is because proper preprocessing of imbalanced data can enable researchers to reduce defects as much as possible, which, in turn, may lead to the elimination of defects in existing data sets. Despite the remarkable achievements that have been accomplished in machine learning studies, systematic literature reviews of imbalanced data preprocessing techniques are lacking. Consequently, there are a limited number of systematic literature review studies on imbalanced data preprocessing. In this study, the authors assess the existing literature to identify the key issues related to data quality and handling and to provide a convenient collection of the techniques used to address these issues when performing data preprocessing. They applied a systematic literature review method involving a manual search to select articles published from January 2010 to September 2018 for review. The qualities of the existing studies were assessed using certain quality assessment criteria. Of the 118 relevant studies found, only 2% were identified as having been conducted following systematic literature review guidelines. This study, therefore, calls for more systematic literature review studies on data preprocessing to improve the quality of the data applied in machine learning studies.

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Section: What Are the Limitations Of Current Research?mentioning

confidence: 99%

Systematic literature review of preprocessing techniques for imbalanced data

Felix

Lee

2019

IET softw.

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“…Only one heart disease database is used for the proposed technique, and more data sets are required for verification of results. Tavares et al (2013) proposed an HDPS for children. The raw data are preprocessed to make it use for classification models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Tavares et al () proposed an HDPS for children. The raw data are preprocessed to make it use for classification models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multicriteria Weighted Vote‐Based Classifier Ensemble for Heart Disease Prediction

Bashir

Qamar

Khan

2015

Computational Intelligence

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The availability of a large amount of medical data leads to the need of intelligent disease prediction and analysis tools to extract hidden information. A large number of data mining and statistical analysis tools are used for disease prediction. Single data-mining techniques show acceptable level of accuracy for heart disease diagnosis. This article focuses on prediction and analysis of heart disease using weighted vote-based classifier ensemble technique. The proposed ensemble model overcomes the limitations of conventional data-mining techniques by employing the ensemble of five heterogeneous classifiers: naive Bayes, decision tree based on Gini index, decision tree based on information gain, instance-based learner, and support vector machines. We have used five benchmark heart disease data sets taken from UCI repository. Each data set contains different set of feature space that ultimately leads to the prediction of heart disease. The effectiveness of proposed ensemble classifier is investigated by comparing the performance with different researchers' techniques. Tenfold cross-validation is used to handle the class imbalance problem. Moreover, confusion matrices and analysis of variance statistics are used to show the prediction results of all classifiers. The experimental results verify that the proposed ensemble classifier can deal with all types of attributes and it has achieved the high diagnosis accuracy of 87.37%, sensitivity of 93.75%, specificity of 92.86%, and F -measure of 82.17%. The F -ratio higher than the F -critical and p-value less than 0.01 for a 95% confidence interval indicate that the results are statistically significant for all the data sets.

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“…At this point, the reasoning is usually powered by classifiers or distance metrics [11]. Some of them are investigating methods of automatic pre-processing and refinement of input data in order to leverage classifier accuracy [20]. Other are focusing on parameters tuning in order to cope with effective training and data noises [3].…”

Section: Related Workmentioning

confidence: 99%

Retrieval of Distinctive Regions of Interest from Video Surveillance Footage: A Real Use Case Study

Mitrea¹,

Piatrik²,

Neville³

2015

6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15)

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The article addresses the issue of retrieving distinctive regions of interest or patterns (DROP) in video surveillance datasets. DROP may include logos, tattoos, color regions or any other distinctive features that appear recorded on video. These data come in particular with specific difficulties such as low image quality, multiple image perspectives, variable lighting conditions and lack of enough training samples. This task is a real need functionality as the challenges are derived from practice of police forces. We present our preliminary results on tackling such scenario from Scotland Yard, dealing with the constraints of a real world use case. The proposed method is based on two approaches: employment of a dense SIFT-based descriptor (Pyramidal Histogram of Visual Words), and use of image segmentation (Mean-Shift) with feature extraction on each segment computed. Tested on real data we achieve very promising results that we believe will contribute further to the ground development of advanced methods to be applied and tested in real forensics investigations.

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Preprocessing unbalanced data using weighted support vector machines for prediction of heart disease in children

Cited by 9 publications

References 28 publications

Systematic literature review of preprocessing techniques for imbalanced data

Systematic literature review of preprocessing techniques for imbalanced data

A Multicriteria Weighted Vote‐Based Classifier Ensemble for Heart Disease Prediction

Retrieval of Distinctive Regions of Interest from Video Surveillance Footage: A Real Use Case Study

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