Obtaining interpretable fuzzy classification rules from medical data

Nauck, Detlef; Kruse, Rudolf

doi:10.1016/s0933-3657(98)00070-0

Cited by 312 publications

(121 citation statements)

References 17 publications

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“…Approaches in research which apply the use of combined techniques such as the current one include neuro fuzzy methods (Nauck & Kruse, 1999), the appli cation of genetic algorithms (GAs) for rule selection (Ishibuchi, Nakashima, & Murata, 1999), or the unification of genetic algo rithms with fuzzy clustering techniques (Roubos & Setnes, 2000). Other methods apply a single approach, applying neural networks, GAs, or fuzzy inference systems (e.g., Jang, 1993;Pena Reyes & Sip per, 1999;Song, Lee, Kim, & Park, 2005).…”

Section: Related Workmentioning

confidence: 99%

ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements

García-Crespo

Rodríguez

Mencke

et al. 2010

Expert Systems with Applications

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a b s t r a c tMedical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge based technologies which avoid ambiguity, such as ontologies rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which applies the aforementioned strat egies. The architecture and proof of concept implementation is described, and results of the evaluation are discussed.

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Section: Related Workmentioning

confidence: 99%

ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements

García-Crespo

Rodríguez

Mencke

et al. 2010

Expert Systems with Applications

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“…[2] made a comprehensive study among several algorithms and their fusion classifier of decision trees and MLP yielded the best accuracy while using PCA for feature reduction. [11] had applied the supervised fuzzy clustering technique and reported an accuracy of 95.57%. [12] introduced a system based on association Rules and neural networks of accuracy equals to 97.4%.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Breast Cancer using Random Forest, Support Vector Machines and Naïve Bayes

El-Gedawy¹

2017

ijecs

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Machine learning techniques can be used to judge important predictor variables in medical datasets. This paper applies three machine learning techniques: Naïve Bayes, SVM and Random Forest to Wisconsin Breast Cancer Database. The three developed models predict whether the patients' trauma are benign or malignant. The paper aims at comparing the performance of these three algorithms through accuracy, precision, recall and f-measure. Results show that Random Forest yields the best accuracy of 99.42%, which is slightly better than both SVM and Naïve Bayes that have accuracies of 98.8% and 98.24% respectively. These results are very competitive and can be used for diagnosis, prognosis, and treatment.

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“…Variable selection based on: the output sensitivity to the input change [25,34], the output sensitivity combined with the correlation between variables [24], Fisher's interclass separability measure [35], variable correlation with the output [36] are the most popular variable selection techniques applied. However, quite often, variable selection is not considered at all [26,27,23].…”

mentioning

confidence: 99%

“…The combined optimization of both structure and parameters has also been considered by applying genetic algorithms (GA) [40][41][42][43][44], unsupervised and reinforcement learning [45], or simple heuristics [36]. To reduce the evolution time in the GA-based technique, Chen et al [46] proposed gathering similar chromosomes into k clusters and then using a representative chromosome in the evaluation process.…”

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confidence: 99%

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A general framework for designing a fuzzy rule-based classifier

et al. 2010

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This is an accepted version of a paper published in Knowledge and Information Systems. This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.Citation for the published paper: verikas, a., guzaitis, j., gelzinis, a., bacauskiene, m. Abstract This paper presents a general framework for designing a fuzzy rule-based classifier. Structure and parameters of the classifier are evolved through a two-stage genetic search. To reduce the search space, the classifier structure is constrained by a tree created using the evolving SOM tree algorithm. Salient input variables are specific for each fuzzy rule and are found during the genetic search process. It is shown through computer simulations of four real world problems that a large number of rules and input variables can be eliminated from the model without deteriorating the classification accuracy. By contrast, the classification accuracy of unseen data is increased due to the elimination.

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Obtaining interpretable fuzzy classification rules from medical data

Cited by 312 publications

References 17 publications

ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements

ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements

Prediction of Breast Cancer using Random Forest, Support Vector Machines and Naïve Bayes

A general framework for designing a fuzzy rule-based classifier

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