Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Wosiak, Agnieszka; Zakrzewska, Danuta

doi:10.15439/2014f296

Cited by 7 publications

(3 citation statements)

References 21 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reduction of large datasets will also be carried out through decreasing the number of analyzed parameters (dimensions) or by way of reducing the number of analyzed instances. The dimensionality reduction can be carried out via statistical strategies, basically Principal Component Analysis (PCA) [11] or via using characteristic variety techniques [14,15]. Dataset cardinality aid can also be accomplished through sampling, grouping or example selection methods [16].…”

Section: Proposed Methodologymentioning

confidence: 99%

Novel Multistage approach of Medical Data using Machine Learning with Clustering Technique

Sasirekha¹

2020

IJATCSE

View full text Add to dashboard Cite

Medical knowledge collection from other resources, classification of information in keeping with their belongings, characteristics and arranging the tips the sub team is essential. Due to this reason why, to into effect the data mining and gadget leaning techniques are helpful to get the consumer decisions. Machine learning techniques can also be categorized into two categories, known as supervised learning and unsupervised studying. This analysis work, focus on to investigate clusters of medical information acquired by the use of unsupervised clustering ways and examine the efficiency via classification set of rules at the medical knowledge.New characteristic variety is accomplished in the class of the dimensionality aid supervised approach referred to as igPCA that attempts to choose a subset of the predictor features in line with the guidelines achieve. After that the easy k-means clustering set of rules randomly chooses K segments as centroids of clusters at the initialization stage. This proposed manner introduce the consensus serve as with the combo of CSPA, HGPA, and MCLA, which might be used to combine unbiased clustering into one final consensus clustering. The result of consensus clustering is used to train the classification set of rules known as SVM. The performance of the classification algorithm on the ultimate degree is crucial for figuring out the effectiveness of the classification set of rules. Performance analysis may also be thought to be a sign of the reliability, quality and balance of the combined consensus clustering.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Novel Multistage approach of Medical Data using Machine Learning with Clustering Technique

Sasirekha¹

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…The algorithms divide all objects into a predetermined number of groups in a manner that maximizes a similarity function. There are two different approaches, that are commonly used in medical studies ( [35] and [36]): the Expectation Maximization (EM) probabilistic method and deterministic k-means algorithm.…”

Section: Unsupervised Learning With Clusteringmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…A feature is irrelevant if it is neither strongly nor weakly relevant. Improving the performance of machine learning classifiers for diagnosis based on feature selection is often applied [10,11]. In this paper additional application of FS methods is investigated.…”

Section: Introductionmentioning

confidence: 99%

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

Paja

2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Melanoma is the most deadly form of skin cancer. Early detection and successful treatment of this disease often is possible. The main goal of this paper is to present results of application of feature selection method to find the most important or all important features that characterize melanocytic spots on the skin and in this way defining of a new Total Dermatoscopy Score formula. Thus, it is possible to decrease dimensionality of that problem. Results gathered during research focus on about six from thirteen descriptive attributes which are the most relevant and are stated as core attributes. Based on these attributes a simple total scoring method could be applied to improve prediction (diagnosis) results, additionally also reducing complexity of problem. Results were acquired by application of six different machine learning algorithms and estimated using several evaluation measures.

show abstract

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Cited by 7 publications

References 21 publications

Novel Multistage approach of Medical Data using Machine Learning with Clustering Technique

Novel Multistage approach of Medical Data using Machine Learning with Clustering Technique

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

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

Contact Info

Product

Resources

About