Classification of medical data with a robust multi-level combination scheme

Tsirogiannis, George L.; Frossyniotis, Dimitrios S.; Stoitsis, John; Golemati, Spyretta; Stafylopatis, Andreas; Nikita, Konstantina S.

doi:10.1109/ijcnn.2004.1381020

Cited by 28 publications

(12 citation statements)

References 11 publications

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“…Don-Sheng Cao [13] combined decision treebased ensemble method with feature selection method backward elimination strategy and bagging, while My ChauTu [14] proposed the integration of bagging, C4.5 algorithm, and Naïve Bayes algorithm in diagnosing cardiovascular disease. Tsirogiannis [15] concluded that bagging results showed higher accuracy compared to methods without bagging. Panwen [16] used the DT algorithm C4.5 with bagging to determine high-frequency electrograph that is abnormal from the data collected from ECG reports of patients.…”

Section: Relevant Literaturementioning

confidence: 99%

An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

Chaudhuri*,

Sinha,

Bhattacharya

et al. 2020

IJRTE

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Cancer leads to most deaths worldwide, and breast cancer isa leading disease that causes death among women. This disease is unique in the way that once treated, can recur in some cases. Individuals are unable to identify their condition before it becomes dangerous. Extracting significant predictive features of breast cancer is an important and risky job for further study. Researchers have applied data mining techniques in medical science. Several authors suggest that a single method doesn't resolve issues in diagnosing problems, and a hybrid model is desirable. In this paper, the authors propose an integrated approachto avoid Type 1 and 2 errors in predicting recurrence. They identify important and contradicting variables and consider them for inclusion and exclusion, respectively, to revise the dataset. The evaluation of findings of crucial methods, using original and revised datasets, widens the choice of identifying the technique with higher accuracy. The results show that the accuracy improves with the selection of variables restricted to the ones identified as relatively significant, and the dataset revised after elimination of contradicting variables.

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Section: Relevant Literaturementioning

confidence: 99%

An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

Chaudhuri*,

Sinha,

Bhattacharya

et al. 2020

IJRTE

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“…MY ChauTu's [24] with the help of bagging algorithm [25] recognized the symptoms of heart disease in patients and estimated the contrast outcomes of decision tree induction method. Classifiers neural network SVM's and decision trees Tsirogiannis used bagging algorithm on the medical data bases.…”

Section: Data Mining In Cancer Researchmentioning

confidence: 99%

A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

Nallamala¹,

Pathuri²,

Koneru³

2018

IJET

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The effective treatment of cancer is not very easy since diagnosis of cancer involves many stages of treatment with gradually changing lifestyles. Physicians play vital role in identifying the correct cause and feel ambiguity for making perfect decisions about hundreds of data available from the internet resource. IDA (Intelligent Data Analysis) which is a part from Data Mining techniques is quiet useful to most of the physicians for decision making about types of cancers. IDA facilitates physicians to classify, detect and analyze the cancer outcome to patients. Healthcare Management System also aids the practitioners to practically search, analyze and compare the result analysis of the patient with existing data in the HMS and guide proper treatment to the cancer affected patient. Health care data analysis comprises enormous data with diversity of health information. One among the most important points that pull down the practitioner's confidence is that utility of latest software and most sophisticated computing machines. This put them in to the state of confusion for proper and elegant decision making for treating the cancer affected patients. Problems in user interaction, lack of awareness in data mining, improper knowledge in electronic guidelines makes physicians to work with old methods of treatment. Traditional medical practicing and modern methods of computing do not match either because of ignorance. IDA and HMS have significant impact for cancer treatment with speedy diagnosis and faster recovery. This also shows great impact on costs, clinical outcomes and proper guidelines for clinical approach. The prime motto of this survey article is to analyze the survey application, bring out the importance of comparison strategies of IDA to improve decision making for medical practitioner for effective cancer treatment.

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“…[7] applied bagging algorithm on medical databases using the classifiers -Neural Networks, SVM'S and Decision Trees. Usage of Bagging proved improved accuracy than without Bagging.…”

Section: Background 21 Overview Of Related Workmentioning

confidence: 99%

Ensemble Decision Making System for Breast Cancer Data

Lavanya¹,

Rani²

2012

IJCA

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Data Mining is a technique to extract the hidden knowledge of information. Among several data mining methods classification is especially useful in the field of medical diagnosis for decision making. In this study, a hybrid approach: CART decision tree classifier with feature selection and boosting ensemble method has been considered to evaluate the performance of classifier. Various Breast cancer data sets are considered for this study as breast cancer is one of the leading causes of death in women.

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Classification of medical data with a robust multi-level combination scheme

Cited by 28 publications

References 11 publications

An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

Ensemble Decision Making System for Breast Cancer Data

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