Decision tree classifiers for automated medical diagnosis

Azar, Ahmad Taher; El-Metwally, Shereen M.

doi:10.1007/s00521-012-1196-7

Cited by 165 publications

(79 citation statements)

References 59 publications

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“…There is no incoming edges for the root of the tree and every internal node have outgoing edges with an incoming edge [17]. We apply binary …”

Section: Decision Trees For Independent Observationsmentioning

confidence: 99%

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Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

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This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

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“…There is no incoming edges for the root of the tree and every internal node have outgoing edges with an incoming edge [17]. We apply binary …”

Section: Decision Trees For Independent Observationsmentioning

confidence: 99%

“…There is no incoming edges for the root of the tree and every internal node have outgoing edges with an incoming edge [17]. We apply binary decision trees in this study so that every node has outgoing edges either with number zero or two.…”

Section: Decision Trees For Independent Observationsmentioning

confidence: 99%

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

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show abstract

“…Some cancers are inevitably overlooked, but having two radiologists looking at each film has been shown to improve the detection rate by as much as 15 %, according to studies cited in the Institute of Medicine report [13]. Computer-aided detection systems evaluate the conspicuous structures and helps in reducing the false positives and false negatives [14,15]. This allows the radiologist to draw conclusions about the condition of the pathology.…”

Section: Breast Cancer Signs In Digital Mammogramsmentioning

confidence: 99%

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

Dheeba

Singh²

2014

Studies in Computational Intelligence

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Breast cancer is the common form of cancer and leading cause of mortality among woman, especially in developed countries. In western countries about 53-92 % of the population has this disease. As with any form of cancer, early detection and diagnosis of breast cancer can increase the survival rate. Mammography is the current diagnostic method for early detection of breast cancer. Breast parenchymal patterns are not stable between patients, between left and right breasts, and even within the same breast from year to year in the same patient. Breast cancer has a varied appearance on mammograms, from the obvious spiculated masses, to very subtle asymmetries noted on only one view, to faint calcifications seen only with full digital resolution or a magnifying glass. The large volume of cases requiring interpretation in many practices is also daunting, given the number of women in the population for whom yearly screening mammography is recommended. It seems obvious that this difficult task could likely be made less error prone with the help of computer algorithms. Computer-aided detection (CAD) systems have been shown to be capable of reducing false-negative rates in the detection of breast cancer by highlighting suspicious masses and microcalcifications on mammograms. These systems aid the radiologist as a 'second opinion' in detecting cancers and the final decision is taken by the radiologist. A supervised machine learning algorithm is investigated-Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of abnormalities in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture based features of the abnormal

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“…The general model classifies a set of symptom data into one of several predefined categories of disease for cases of medical diagnosis. A decision tree [4][5] is a classic algorithm in the medical classification domain, one that uses the information entropy method; however, it is sensitive to inconsistencies in the data. The support vector machine [6][7][8] has a solid theoretical basis for the classification task; because of its efficient selection of features, it has higher predictive accuracy than decision trees.…”

Section: Introductionmentioning

confidence: 99%

Learning and inference in knowledge-based probabilistic model for medical diagnosis

Jiang

Li²,

Zhao

et al. 2017

Knowledge-Based Systems

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Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis. When a set of symptoms is activated for a specific patient, we can generate a ground medical knowledge network composed of symptom nodes and potential disease nodes. By Incorporating a Boltzmann machine into the potential functio n of a Markov network, we investigated the joint probability distribution of the MKN. In order to deal with numerical symptoms, a multivariate inference model is presented that uses conditional probability. In addition, the weights for the knowledge graph were efficie ntly learned from manually annotated Chinese Electronic Medical Records (CEMRs). In our experiments, we found numerically that the optimum choice of the quality of disease node and the expression of symptom variable can improve the effectiveness of medical diagnosis. Our experimental results comparing a Markov logic network and the logistic regression algorithm on an actual CEMR database indicate that our method holds promise and that MKN can facilitate studies of intelligent diagnosis.

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Decision tree classifiers for automated medical diagnosis

Cited by 165 publications

References 59 publications

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

Learning and inference in knowledge-based probabilistic model for medical diagnosis

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