Combined numerical and linguistic knowledge representation and its application to medical diagnosis

Meesad, Phayung; Yen, Gary G.

doi:10.1109/tsmca.2003.811290

Cited by 72 publications

(26 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Traditionally the diagnostic accuracy of a patient depends on a physician's experience [1], however, this expertise is built up over many years of observations of different patients' symptoms and confirmed diagnoses. Even then the accuracy still cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

200

View full text Add to dashboard Cite

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

200

View full text Add to dashboard Cite

show abstract

“…This model aimed for more generalized expert system trained using neural networks. A novel hybrid intelligent system (HIS) proposed by Phayung Meesad and Gary G. Yen [14], provides a unified integration of numerical and linguistic knowledge representation. A I.J.…”

Section: A Existing Knowledge Representation Modelsmentioning

confidence: 99%

Towards MORK: Model for Representing Knowledge

Kamala¹,

Selwyn²

2016

IJMECS

View full text Add to dashboard Cite

Abstract-Smart world needs intelligent system for effective and timely decision making. This is achieved only through a knowledge based system with functional knowledge representation units. In this paper, two models are proposed for representing knowledge. This process involves in getting the data and placing the information in the correct location. Logical notations are used for taking the clauses and graph is used for putting the entities. In Model one, the data is translated into logical statements using predicate logics, later the knowledge is stored in conceptual graph and retrieved. Whereas in Model two, the given information is translated using First Order Logic (FOL), by applying description logic concept rules are defined and as a result reasoning is done. Storage is done by using concept-relation graph. The main aims of our models are to have easy and simple access over the information. These models return the required exact answer, for the higher order query posted by the end user to the intelligent system.

show abstract

“…Technique Classification rate (%) [3] IGANFIS 98.24 [32] SANFIS 96.07 [10] L.V .Q 95,82 [15] Fuzzy 96,71 [21] Fuzzy-GA1 97,36 We can say that neuro-fuzzy systems are connectionist models that allow learning as artificial neural network , but their structure can be interpreted as a set of fuzzy rules .Fuzzy logic and neural networks form the basis of the majority aided diagnostic intelligent systems.It would be interesting to combine the two approaches to exploit both advantages.…”

Section: Referencesmentioning

confidence: 99%

A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

Bekaddour¹

2012

IJCSIT

View full text Add to dashboard Cite

Breast cancer is known as one of the most common cancers to afflict the female population. Computer assisted diagnosis can be helpful for doctors in detection and diagnosing of potential abnormalities. Several techniques can be useful for accomplishing this task. This paper outlines an approach for recognizing breast cancer diagnosis using neuro-fuzzy inference technique namely ANFIS (Adaptative Neuro-Fuzzy Inference System). Wisconsin breast cancer diagnosis (WBCD)database developed at University of California, Irvine (UCI) is used to evaluate this method. Results show that the best performances are obtained by our model compared to others cited in literatur (an accuracy of 98, 25 % ).

show abstract

Combined numerical and linguistic knowledge representation and its application to medical diagnosis

Cited by 72 publications

References 45 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Towards MORK: Model for Representing Knowledge

A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

Contact Info

Product

Resources

About