Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms-Reference-Cited by-同舟云学术

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms

Published:2024-03-21 Issue: Volume: Page:1-38
ISSN:0954-898X
Container-title:Network: Computation in Neural Systems
language:en
Short-container-title:Network: Computation in Neural Systems

Author:

Ejiyi Chukwuebuka Joseph¹,Qin Zhen¹,Ukwuoma Chiagoziem Chima¹,Nneji Grace Ugochi²,Monday Happy Nkanta²,Ejiyi Makuachukwu Bennedith³,Ejiyi Thomas Ugochukwu⁴,Okechukwu Uchenna³,Bamisile Olusola O.⁵

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. Software Engineering Department, Sino-British Collaborative Education, Chengdu University of Technology, Oxford Brookes University, Chengdu, China

3. Pharmacy Department, University of Nigeria Nsukka, Enugu, Nigeria

4. Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Enugu, Nigeria

5. Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre, Chengdu University of Technology, Chengdu, China

Funder

National Natural Science Foundation of China

the Frontier Science and Technology Innovation Projects of National Key R&D Program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/0954898X.2024.2331506

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1. Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

2. Machine learning-based heart disease diagnosis: A systematic literature review

3. To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification

4. A New Big Data Feature Selection Approach for Text Classification

5. Diagnostic Framework for Automatic Classification and Visualization of Alzheimer’s Disease with Feature Extraction Using Wavelet Transform

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