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Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review

Siti utami Dewi, Rr. Tutik Sri Haryati
2022 Jurnal Ilmiah Keperawatan Orthopedi  
Results: Based on a review of 10 journals using the Fuzzy Inference System (FIS) Mamdani model can provide effective results to identify risks for people who have the possibility of developing breast cancer  ...  Conclusion: The fuzzy expert system using the Mamdani method can be applied to the problem domain of breast cancer diagnosis with a fairly good level of accuracy to be able to overcome the inequality of  ...  Acknowledgement The authors express their infinite gratitude to the Faculty of Nursing, University of Indonesia for the opportunity to facilitate the authors to complete this literature review.  ... 
doi:10.46749/jiko.v5i2.70 fatcat:bamvjaqk6vakbivvy257q2ec74

Breast Cancer Diagnosis Based on Genetic-fuzzy Logic and ANFIS Using WBCD

Amany Mostafa Lotayef, Khaled H. Ibrahim, Rania Ahmed Abul Seoud
2020 International Journal of Fuzzy Logic Systems  
The first method is a combining of two major methodologies, namely the fuzzy based systems and the evolutionary genetic algorithms (GFIS).  ...  The second method intends to an integrated view of implementing an adaptive neuro-fuzzy inference system (ANFIS) with feature selection using principle component analysis (PCA).  ...  Then, the proposed method uses Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based  ... 
doi:10.5121/ijfls.2020.10202 fatcat:6rezufpaoje4xcqdkyuupakgye

Developing of Fuzzy Logic Decision Support for Management of Breast Cancer

Sameh Mohamed, Wael Mohamed
2016 International Journal of Computer Applications  
This paper aims to describe an intelligent procedure based on fuzzy logic techniques and medical model to detect and diagnose Breast.  ...  We have used Mamdani inference engine to deduce from the input parameters to stage the cancer.  ...  expert oncologist, the cancerous development stage of the detected lesion A fuzzy logic technique for the prediction of the risk of breast cancer based on a set of judiciously chosen fuzzy rules utilizing  ... 
doi:10.5120/ijca2016910585 fatcat:kalnxrlxmjdmrhrdo32srait7e

A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer

2014 International Journal of Engineering  
A fuzzy expert system models knowledge as a set of explicit linguistic rules and performs reasoning with words.  ...  This research presents a fuzzy expert system for breast cancer prognosis. This approach is capable enough to capture ambiguity and imprecision prevalent in the characterization of the breast cancer.  ...  logic system [27] 5 Fuzzy 72.1% Fuzzy expert system[This work] 6 Fuzzy 95% CONCLUSIONS This study presented a fuzzy rule-based expert system for prognosis of the breast cancer in healthy and unhealthy  ... 
doi:10.5829/idosi.ije.2014.27.10a.09 fatcat:jx4y2ryy5fbnzagltjsqaskb7u

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Niyazi Senturk, Gulten Tuncel, Berkcan Dogan, Lamiya Aliyeva, Mehmet Sait Dundar, Sebnem Ozemri Sag, Gamze Mocan, Sehime Gulsun Temel, Munis Dundar, Mahmut Cerkez Ergoren
2021 Genes  
In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network  ...  Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications.  ...  Sahria and Mandang (2019) developed a program that could show the risk of breast cancer based on the fuzzy logic method using five histological risk factors for only young women [31] .  ... 
doi:10.3390/genes12111774 pmid:34828379 pmcid:PMC8623958 fatcat:vffqktrhurauderstljy44x3s4

Fuzzy Analysis of Breast Cancer Disease using Fuzzy c-means and Pattern Recognition

Indira Muhic
2013 Southeast Europe Journal of Soft Computing  
The automatic diagnosis of breast cancer is an important, real-world medical problem. In this article is introduced a new approach for diagnosis of breast cancer.  ...  The proposed approach uses Fuzzy c-means (FCM) algorithm and pattern recognition method. Algorithm has been applied to breast cancer clinic instances obtained from the University of Wisconsin.  ...  Furthermore the greatest advantage of using fuzzy logic lies in the fact that scientists can model non-linear, imprecise, complex systems by implementing human experience, knowledge and practice as a set  ... 
doi:10.21533/scjournal.v2i1.45 fatcat:qnleugeyfndz3jxqifyf7k4roq

Breast Cancer Classification Enhancement Based on Entropy Method

Ali Salem Ali Bin Sama, Saeed Mohammed Saeed Baneamoon
2017 International Journal of Engineering and Applied Computer Science  
fuzziness, while at mass phase entropy enhances EA for the classification of masses in mammogram images.  ...  EA and fuzzy logic are applied at training phase for parameters tuning, however, entropy method is applied at training phase and localization phase, where at training phase entropy enhances indicator of  ...  Hence, for breast cancer classification, this paper presents a method by using EA, fuzzy logic and entropy method, where entropy method combine with fuzzy logic at training phase and with EA at mass phase  ... 
doi:10.24032/ijeacs/0208/06 fatcat:hmohzvvuznelplhbnn7ugglgr4

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Ebrahem A. Algehyne, Muhammad Lawan Jibril, Naseh A. Algehainy, Osama Abdulaziz Alamri, Abdullah K. Alzahrani
2022 Big Data and Cognitive Computing  
In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed  ...  for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively.  ...  Acknowledgments: The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia and University of Tabuk, Tabuk71491, Saudi Arabia as well.  ... 
doi:10.3390/bdcc6010013 fatcat:7xqy3q4bb5huxbdljzj5dgnpw4

A comparative study of fuzzy classification methods on breast cancer data

R. Jain, A. Abraham
2004 Australasian physical & engineering sciences in medicine  
In this paper, we examine the performance of four fuzzy rule generation methods on Wisconsin breast cancer data.  ...  The other two approaches are based on fuzzy grids with homogeneous fuzzy partitions of each attribute. The performance of each approach is evaluated on breast cancer data sets.  ...  Acknowledgements The authors are grateful to several reviewers (for constructive comments) and a number of readers including Robyn Vast for reading and correcting this paper.  ... 
doi:10.1007/bf03178651 pmid:15712589 fatcat:kfw6lsf6gzd6dcm2tk7hlipjl4

A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

Bekaddour Fatima
2012 International Journal of Computer Science & Information Technology (IJCSIT)  
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.  ...  CONCLUSION This work presents a knowledge extraction and classification of breast cancer disease using basically a neuro-fuzzy approach for system design, able to explain human decisions.  ... 
doi:10.5121/ijcsit.2012.4513 fatcat:cysnjbudirge5bjwnj2oe5jpsi

A Survey on Various Classification Techniques for Clinical Decision Support System

Chaitali Vaghela, Nikita Bhatt, Darshana Mistry
2015 International Journal of Computer Applications  
The main idea of Clinical Decision Support System is a set of rules derived from medical professionals applied on a dynamic knowledge.  ...  Different techniques are used for different diagnosis. In this paper, various classification techniques for clinical decision support system are discussed with example. .  ...  Ribeiro et al 2014 proposed fuzzy breast cancer system to map two controlled and two non-controlled input variable into the risk of breast cancer occurrence.  ... 
doi:10.5120/20498-2369 fatcat:ydu5csg3vzbuhmdan3lkuzjoum

Hybrid Harmony Search And Genetic For Fuzzy Classification Systems

Maryam Sadat Mahmoodi, Seyed Abbas Mahmoodi
2014 Journal of Mathematics and Computer Science  
The algorithm uses Genetic algorithm based local search to improve the quality of fuzzy classification system. The proposed algorithm is evaluated on a breast cancer data.  ...  In this paper, a method based on Harmony Search Algorithm (HSA) is proposed for pattern classification.  ...  For pattern classification problems, several classification methods based on fuzzy set theory have been proposed [9] .  ... 
doi:10.22436/jmcs.010.03.06 fatcat:hlrsbcjjvfe7rfsmsgzyatxdee

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Simarjeet Kaur, Jimmy Singla, Lewis Nkenyereye, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi, Shaker El-Sappagh, Md. Saiful Islam, S. M. Riazul Islam
2020 IEEE Access  
act as a collection of rules to the knowledge base. • Knowledgebase: This is the main component of the fuzzy logic system.  ...  The overall fuzzy system depends on the knowledge base.  ... 
doi:10.1109/access.2020.3042273 fatcat:yvj2wugkkzdk3dswx4w4eoehjq

A Survey on Case-based Reasoning in Medicine

Nabanita Choudhury, Shahin Ara
2016 International Journal of Advanced Computer Science and Applications  
Case-based reasoning (CBR) based on the memorycentered cognitive model is a strategy that focuses on how people learn a new skill or how they generate hypothesis on new situations based on their past experiences  ...  In this paper, we extensively survey the literature on CBR systems that are used in the medical domain over the past few decades.  ...  eXiT*CBR [84], [85] Diagnosis CBR, Pedigree tools, and Genetic algorithms Breast cancer Adaptation performed Ahmed, Begum & Funk -- [86] Diagnosis CBR, Fuzzy logic, Rule-based reasoning  ... 
doi:10.14569/ijacsa.2016.070820 fatcat:jl6ik24bqveoffimndhjbfq2gy

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Nur Farahaina Idris, Mohd Arfian Ismail, Mohd Saberi Mohamad, Shahreen Kasim, Zalmiyah Zakaria, Tole Sutikno
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection.  ...  Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection.  ...  ACKNOWLEDGEMENTS The authors thank to Universiti Malaysia Pahang, United Arab Emirates University, Universiti Tun Hussein Onn, Universiti Teknologi Malaysia and Universitas Ahmad Dahlan for supporting  ... 
doi:10.11591/ijai.v11.i2.pp448-461 fatcat:v2x5comvkren5nsoplubs2pofu
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