ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Integrating Neuro-fuzzy Systems for Enhanced Cancer Data Analysis and Prediction


Affiliations
1 Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, India
2 Department of Electronics and Communication Engineering, T.J.S. Engineering College, India
3 Department of Electronics and Communication Engineering, Government Engineering College, Jhalawar, India
4 Department of Electronics and Communication Engineering, IIMT College of Engineering, India
5 College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • M. Bhende, S. Shinde and V. Saravanan, “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-14, 2022.

  • K. Sakthisudhan, B. Murugesan and P.N.S. Sailaja, “Textile EF Shaped Antenna based on Reinforced Epoxy for Breast Cancer Detection by Composite Materials”, Materials Today: Proceedings, Vol. 45, pp. 6142-6148, 2021.

  • W. Zhao and Y. Lu, “Serial Fuzzy System Algorithm for Predicting Biological Activity of Anti-Breast Cancer Compounds”, Applied Intelligence, Vol. 53, No. 11, pp. 13801-13814, 2023.

  • R. Pavithra and R. Srinivasan, “Web Service Deployment for Selecting a Right Steganography Scheme for Optimizing both the Capacity and the Detectable Distortion”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 6, No. 4, pp. 267-277, 2018.

  • R. Shesayar and S. Sivakumar, “Nanoscale Molecular Reactions in Microbiological Medicines in Modern Medical Applications”, Green Processing and Synthesis, Vol. 12, No. 1, pp. 1-13, 2023.

  • S. Chidambaram and D. Shreecharan, “Hyperspectral Image Classification using Denoised Stacked Auto Encoder-Based Restricted Boltzmann Machine Classifier”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 213-221, 2022.

  • R. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent based Diagnosis for Skin Disease using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-14, 2022.

  • L.C. Voumik and A. Dutta, “A Study on Mathematics Modeling using Fuzzy Logic and Artificial Neural Network for Medical Decision Making System”, Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solutions, pp. 492-498, 2023.

  • H. Ouifak and A. Idri, “On the Performance and Interpretability of Mamdani and Takagi-Sugeno-Kang based Neuro-Fuzzy Systems for Medical Diagnosis”, Scientific African, Vol. 20, pp. 1-13, 2023.

  • S. Chandragandhi, G. Dhiman and A. Kaur, “Analysis of Protein-Ligand Interactions of SARS-Cov-2 against Selective Drug using Deep Neural Networks”, Big Data Mining and Analytics, Vol. 4, No. 2, pp. 76-83, 2021.

  • S. Sundaramurthy, A. Thangavelu and K. Sekaran, “Predicting Rheumatoid Arthritis from the Biomarkers of Clinical Trials using Improved Harmony Search Optimization with Adaptive Neuro-Fuzzy Inference System”, Journal of Intelligent and Fuzzy Systems, Vol. 44, No. 1, pp. 125-137, 2023.

  • C.H. Yang and L.Y. Chuang, “Overall Mortality Risk Analysis for Rectal Cancer using Deep Learning-based Fuzzy Systems”, Computers in Biology and Medicine, Vol. 157, pp. 106706-106714, 2023.

  • S. Goktepe Yıldız, “Prediction of Students’ Perceptions of Problem Solving Skills with a Neuro-Fuzzy Model and Hierarchical Regression Method: A Quantitative Study”, Education and Information Technologies, Vol. 67, pp. 1-39, 2023.


Abstract Views: 37

PDF Views: 1




  • Integrating Neuro-fuzzy Systems for Enhanced Cancer Data Analysis and Prediction

Abstract Views: 37  |  PDF Views: 1

Authors

R. Nandhakumar
Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, India
M. Sathya Priya
Department of Electronics and Communication Engineering, T.J.S. Engineering College, India
Rotash Kumar
Department of Electronics and Communication Engineering, Government Engineering College, Jhalawar, India
Dinesh Kumar Yadav
Department of Electronics and Communication Engineering, IIMT College of Engineering, India
Mohammed Saleh Al Ansari
College of Engineering, Department of Chemical Engineering, University of Bahrain, Bahrain

Abstract


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.

References