A New Method for Diagnosing Breast Cancer using Firefly Algorithm and Fuzzy Rule based Classification

Sadeghzadeh, Mehdi

doi:10.1109/icaict.2017.8687061

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Al-Dmour et al presented the usage of fuzzy logic techniques in a warning system to categorize patients’ status or medical conditions [ 32 ]. RBS and FRBS have also been used to develop many MDSS in recent decades [ 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. These systems represent the symptoms of MDSS patients and are based on an inference algorithm to process the information using linguistic terms.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications

Czmil

2023

Sensors

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The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.

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Section: Related Workmentioning

confidence: 99%

Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications

Czmil

2023

Sensors

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Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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