Medical diagnosis support and accuracy improvement by application of total scoring from feature selection approach

Paja, Wiesław

doi:10.15439/2015f361

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…More and more data are collected either by interviews, equipment [39] or extraction from text [45], speech [46] or images [47], including medical imaging [48]. In high-dimensional datasets missing data may be more frequent [49] and appropriate feature selection technique [50], [51] may improve the imputation accuracy [10]. Novel solutions of outlier detection based on linguistically quantified statements may be also considered to remove impurities from the data [52].…”

Section: Discussionmentioning

confidence: 99%

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Wosiak

Glinka

Zamecznik

et al. 2018

Annals of Computer Science and Information Systems

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The paper describes the study on the problem of missing values in medical data collected to discover new dependencies between parameters in children born with intrauterine growth restriction disorder. The aim of the research is to propose a procedure that may be taken to improve the medical inference in the presence of missing data. The approach with use of unconditional mean and k-nearest neighbor imputation has been applied. The experiments proved that application of missing data imputation in original dataset yields more valuable dependencies when compared to original data, maintaining the confidence interval for goodness of fit with the original distribution above 90%. The discovered dependencies in data may establish the basis for new treatment procedures of children with intrauterine growth restriction disorder.

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Section: Discussionmentioning

confidence: 99%

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Wosiak

Glinka

Zamecznik

et al. 2018

Annals of Computer Science and Information Systems

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show abstract

“…Another aspect is a computing cost when handling large volume of data with multivariate features which brings the necessity of good feature selection or principal component analysis [34]- [36]. Also, multi-class imbalanced problems…”

Section: Discussionmentioning

confidence: 99%

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Wosiak

Karbowiak

2017

Annals of Computer Science and Information Systems

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Abstract-The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance. The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance. To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and compared. The experiments were conducted on five datasets -three binary and two multiclass. They comprise several data preprocessing methods applied on data and the classification with different techniques. The study shows that for some datasets there exists a combination of a certain preprocessing method and a classification technique which outperforms other approaches. For datasets with complex distribution or too many features the ratio of correctly predicted labels may be low regardless what resampling method and classification technique has been applied.

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“…Many research papers concern cancer diagnosis, appropriate feature selection techniques [1]- [3], and its classification based on gene expression [4]. A big challenge is an accurate classification of medical imaging and sound recordings (see [5] and [6]).…”

Section: Introductionmentioning

confidence: 99%

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

Wosiak¹,

Zamecznik²,

Niewiadomska-Jarosik³

2016

Annals of Computer Science and Information Systems

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Abstract-This paper concerns automated identification of intrauterine growth restriction (IUGR) types by use of machine learning methods. The research presents a comparison of supervised and unsupervised learning covering single and hybrid classification, as well as clustering. Supervised learning techniques included bagging with Naïve Bayes, k-nearest neighbours (kNN), C4.5 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with Naïve Bayes, SMO, kNN and C4.5 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies: kNN, C4.5, Naïve Bayes, random tree and sequential minimal optimization algorithm for training support vector machines. Unsupervised learning encompassed k-means and expectation-maximization algorithms. The major conclusion drawn from the study was that hybrid classifiers have demonstrated their potential ability to identify more accurately symmetrical and asymmetrical types of IUGR, whereas the unsupervised learning techniques produced the worst results.

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Medical diagnosis support and accuracy improvement by application of total scoring from feature selection approach

Cited by 7 publications

References 12 publications

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

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