A fuzzy rule‐based system with decision tree for breast cancer detection

Gupta, Vishal; Gaur, Harshit; Vashishtha, Srishti; Das, Uttirna; Singh, Vivek Kumar; Hemanth, D. Jude

doi:10.1049/ipr2.12774

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…Fuzzy decision trees are used to tackle theoretical and realworld machine-learning challenges. They have been the subject of several related studies [2,4,5,14,17]. The research results demonstrate the advantages of tree structure for modeling machine learning solutions.…”

Section: Tree Structurementioning

confidence: 76%

“…The fundamental idea is to create a node at each level of the hierarchy for each class, with each node denoting an oblique geometric structure represented by a fuzzy rule. The tree structure significantly improves the rule-based system which is used to characterize uncertain data [19] applied to classification problems in many fields [1,4,3,16].…”

Section: Introductionmentioning

confidence: 99%

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The Novel CFRG -Based Complex Fuzzy Transfer Learning System

Huong,

Lan

2024

JCC

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Today, the rapid development of the internet has led to a data explosion; the complex fuzzy transfer learning (CFTL) model has received increasing attention from the academic community due to its various real-world applications, such as solar activity, digital signal processing, time series forecasting, etc. CFTL combines Transfer learning and Complex Fuzzy Logic in a framework to solve the problem of learning tasks with no prior direct contextual knowledge, which is stored, retrieved, and organized in the data structure. Data structures play an important role in computational intelligence because they are key performance indicators for systems or models. Therefore, to improve the performance of the previous CFTL, this paper investigates a novel complex fuzzy decision tree (CFDT) structure to represent the complex fuzzy rules and provides a transfer learning model for a complex fuzzy inference system. In contrast with prior axis-parallel decision trees in which only a single feature or variable is considered at each node, the node of the proposed decision tree structures includes complex fuzzy inference rules that contain multiple elements. Multiple features for each node help minimize the size. To prove the efficiency of the proposed framework, we carry out extension experiments on numerous instances (datasets). Experimental results demonstrate/exhibit that our offered performs better than the prior framework regarding accuracy and the size of the produced trees.

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Section: Tree Structurementioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

The Novel CFRG -Based Complex Fuzzy Transfer Learning System

Huong,

Lan

2024

JCC

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“…Gupta et al [11] develop a fuzzy system for breast cancer classification. This work used a decision tree to derive the rules to be used in the fuzzy system with a total of 27 rules and achieved an accuracy of 97.00% with the system with those rules.…”

Section: Related Workmentioning

confidence: 99%

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

Cardoso,

Medeiros,

Vellasco

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Breast cancer is the most common cancer diagnosed in the world, being the cause of death of 685,000 people worldwide in 2020. Due to the aggressiveness of the disease, early-stage identification, treatment, and remission detection are important to ensure longevity to those who may have cancer. In this paper, we propose a fuzzy-genetic approach for breast cancer recurrence classification. To this end, we use a Genetic Algorithm to design automatically the fuzzy inference system with the objective of balancing between accuracy and explainability. The proposed system achieved an accuracy of 91.30%, finding eleven rules with a maximum of three antecedents per rule, which provided a competitive result compared to other Machine Learning approaches.

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An effective classification approach to categorize the breast cancer using modified support vector machine as a classifier

Muvva,

Dayanand Lal,

Shah

2023

Internet Technology Letters

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Breast cancer is a type of cancer which threatens the health of women and occupies first in female cancer mortality. In recent days, the machine learning techniques are widely utilized in various medical fields particularly in the application related to detection and classification. However, the accuracy obtained from the existing machine learning techniques was reliably low and results in poor classification. To overcome the issues in existing algorithms, this research introduced a Modified Support Vector Machine (MSVM) to classify the breast cancer as benign and malignant. The input data is obtained from Wisconsin Breast Cancer Dataset (WBCD) and the pre‐processing is performed to remove the inappropriate information from the raw data. Then, Principle Component Analysis (PCA) is used in the process of dimensionality reduction. The reduced dimensions are subjected into the stage of feature extraction using linear Discriminant analysis (LDA) and finally the classification is performed using the proposed MSVM. The experimental results show that the proposed approach achieved better classification accuracy of 99.34% which is comparatively higher than the existing Deer canid based Deep Convolutional Neural Network (DCNN), LDA‐SVM, Fuzzy rule based system and Intelligent Ensemble Classification method on the basis of Multi‐Layer Perceptron (IEC‐MLP) with classification accuracy of 98.12%, 97.65%, 97.22% and 98.74% respectively.

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A fuzzy rule‐based system with decision tree for breast cancer detection

Cited by 12 publications

References 43 publications

The Novel CFRG -Based Complex Fuzzy Transfer Learning System

The Novel CFRG -Based Complex Fuzzy Transfer Learning System

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

An effective classification approach to categorize the breast cancer using modified support vector machine as a classifier

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