Rough Set Approach for Generation of Classification Rules of Breast Cancer Data

Hassanien, Aboul Ella; Ali, Jafar

doi:10.15388/informatica.2004.043

Cited by 31 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prediction accuracy, sensitivity, and specificity are some of the evaluation models that are used to estimate the performance of the proposed system. Hassanien and Ali (2004) presented a Rough Set method for generating classification rules. This study showed that the theory of Rough Sets seems to be a useful tool.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Ghorbani¹,

Ghousi²

2019

10.5267/j.ijdns

View full text Add to dashboard Cite

Due to the increasing technological advances in all fields, a considerable amount of data has been collected to be processed for different purposes. Data mining is the process of determining and analyzing hidden information from different perspectives to obtain useful knowledge. Data mining can have many various applications, one of them is in medical diagnosis. Today, many diseases are regarded as dangerous and deadly. Heart disease, breast cancer, and diabetes are among the most dangerous ones. This paper investigates 168 articles associated with the implementation of data mining for diagnosing such diseases. The study concentrates on 85 selected papers which have received more attention between 1997 and 2018. All algorithms, data mining models, and evaluation methods are thoroughly reviewed with special consideration. The study attempts to determine the most efficient data mining methods used for medical diagnosing purposes. Also, one of the other significant results of this study is the detection of research gaps in the application of data mining in health care.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Ghorbani¹,

Ghousi²

2019

10.5267/j.ijdns

View full text Add to dashboard Cite

show abstract

“…Kumar and Inbarani (2015b) Classification This paper presents the new automated classification method for electrocardiogram (ECG) arrhythmia using improved bijective soft set theory. Hassanien and Jafar (2004) Classification In this paper, presented a rough set-based system for rules generation. The generated rules are from a set of observed 360 samples of the breast cancer data.…”

Section: Feature Selectionmentioning

confidence: 99%

Hybrid system based on bijective soft and neural network for Egyptian neonatal jaundice diagnosis

Azar

Inbarani

Kumar

et al. 2016

IJIEI

View full text Add to dashboard Cite

Neonatal jaundice or hyperbilirubinemia and its evolution to acute bilirubin encephalopathy (ABE) and kernicterus are an important, yet avoidable, origin of newborn deaths, re-hospitalisations and disabilities generally. In this study, a new supervised hybrid bijective soft set neural network-based classification method is introduced for prediction of Egyptian neonatal jaundice dataset. Early prediction and classification of diseases would provide support to doctors for making decision of patient concerning the type of treatment. The hybrid bijective soft set neural network (BISONN) approach integrates both bijective soft set and back propagation neural network for the diagnosis of diseases. The experimental results are acquired by examining the proposed method on neonatal jaundice. The acquired results demonstrate that the hybrid bijective soft set neural network method can deliver expressively more accurate and consistent predictive accuracy than well-known algorithms such as bijective soft set classifier, back propagation network, multi-layered perceptron, decision table and naïve Bayes classification algorithms. areas of, rough set theory, wavelet theory, medical image analysis, astronomical image analysis, biometrics and computational forensics.

show abstract

“…Gupta et al (2011) analyzes the various classification techniques applied to diagnosis and prognosis of breast cancer. The author analyse the papers (Sarvestan et al, 2010;Orlando et al, 2010;Abdelaal et al, 2010;Chang and Liou, 2005;Gandhi et al, 2010;Padmavati, 2011;Chul et al, 2001;Hassanien and Jafar, 2004;Sudhir et al, 2006;Jamarani et al, 2005;Abdelghani and Guven, 2006;Choi et al, 2009;Lundin et al, 1999;Street, 1998;Chi et al, 2007;Dursun et al, 2004;Khan et al, 2008) and concludes that any classification method is acceptable for diagnosis. But for the prognosis ANN classification method gives higher accuracy than any other classification methods.…”

Section: Cancer Datasetmentioning

confidence: 99%

Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

Vanaja¹,

Rameshkumar²

2015

Journal of Computer Science

View full text Add to dashboard Cite

The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

show abstract

Rough Set Approach for Generation of Classification Rules of Breast Cancer Data

Cited by 31 publications

References 15 publications

Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Predictive data mining approaches in medical diagnosis: A review of some diseases prediction

Hybrid system based on bijective soft and neural network for Egyptian neonatal jaundice diagnosis

Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

Contact Info

Product

Resources

About