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Sathya Priya, M.
- Integrating Neuro-fuzzy Systems for Enhanced Cancer Data Analysis and Prediction
Abstract Views :38 |
PDF Views:1
Authors
R. Nandhakumar
1,
M. Sathya Priya
2,
Rotash Kumar
3,
Dinesh Kumar Yadav
4,
Mohammed Saleh Al Ansari
5
Affiliations
1 Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, IN
2 Department of Electronics and Communication Engineering, T.J.S. Engineering College, IN
3 Department of Electronics and Communication Engineering, Government Engineering College, Jhalawar, IN
4 Department of Electronics and Communication Engineering, IIMT College of Engineering, IN
5 College of Engineering, Department of Chemical Engineering, University of Bahrain, BH
1 Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, IN
2 Department of Electronics and Communication Engineering, T.J.S. Engineering College, IN
3 Department of Electronics and Communication Engineering, Government Engineering College, Jhalawar, IN
4 Department of Electronics and Communication Engineering, IIMT College of Engineering, IN
5 College of Engineering, Department of Chemical Engineering, University of Bahrain, BH
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3195-3199Abstract
The abstract for Integrating Neuro-Fuzzy structures for more significant most cancers statistics analysis and Prediction describes research carried out to examine using a sort of artificial intelligence, known as a Neuro-Fuzzy device, to analyze and expect facts from most cancer sufferers. by leveraging the strengths of both Neural Networks and Fuzzy common sense structures, Neuro-Fuzzy systems provide a powerful answer for complicated statistics analysis. This research examines and tests the overall performance of Neuro-Fuzzy systems on hard and fast benchmark datasets from most cancers Toolbox Markup Language (TMX). Consequences showed that Neuro-Fuzzy yielded a higher accuracy charge when compared to different device learning algorithms in studying information from a diverse set of patients. Furthermore, the researchers also stated that Neural-Fuzzy systems were able to discern subtypes of cancer in an affected person population, which had not been formerly feasible with different techniques. The work defined in the abstract could have a long way to attaining implications for the remedy and prognosis of most cancer patients. With the promising results of this, have a look at showing that Neuro-Fuzzy structures are able to distinguish between particular forms of cancer correctly; a more precise treatment plan might be created for people living with cancer. Additionally, with the improved accuracy of Neuro-Fuzzy structures, more excellent dependable predictions will be made about the progression of most cancers in a selected patient, helping doctors to plan treatments. Ordinary, the findings of the research summarized in this summary are especially significant as the advanced accuracy and capacity to figure out subtle differences in most cancer types keep the promise of improved remedies and prognoses for people living with cancer.Keywords
Neuro-Fuzzy Systems, Cancer Data Analysis, Cancer Data Prediction, Machine Learning, Artificial Intelligence.
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