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Research on Cancer Diagnosis Method Based on LightGBM-Gridsearchcv

Published: 19 July 2022 Publication History

Abstract

Cancer has become a non-negligible problem that threatens human health in today's society. The traditional methods of cancer diagnosis usually use cell morphology, histopathology and other methods. Nowadays, the use of machine learning technology to predict cancer has become a new actionable way. This paper proposes the use of machine learning algorithms to assist breast cancer diagnosis, using a variety of algorithms such as LightGBM, Random Forests (RF), Support Vector Machines (SVM), Linear SVM, K-Nearest Neighbor (KNN), and combined with grid search algorithms, respectively constructed intelligent predictive and diagnostic models for malignant breast cancer. Finally, using the breast cancer data set of the University of Wisconsin (WCBD) hospital to conduct experiments, a classification model based on LightGBM-Gridsearchcv is proposed. Compared with the models, the LightGBM-Gridsearchcv model has a recognition accuracy of 95.9% for malignant cancer cases. The machine learning method has put forward a new research idea and method for the diagnosis of breast cancer, and provided a research direction for the promotion of intelligent medical treatment, which has very important practical significance and application value.

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  • (2024)Enhancing Breast Cancer Prediction with XAI-Enabled Boosting Algorithms2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10723843(1-5)Online publication date: 24-Jun-2024
  • (2023)Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PETJournal of Personalized Medicine10.3390/jpm1303053913:3(539)Online publication date: 17-Mar-2023
  1. Research on Cancer Diagnosis Method Based on LightGBM-Gridsearchcv

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    cover image ACM Other conferences
    BDE '22: Proceedings of the 4th International Conference on Big Data Engineering
    May 2022
    139 pages
    ISBN:9781450395632
    DOI:10.1145/3538950
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 July 2022

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    Author Tags

    1. Cancer
    2. Classification
    3. LightGBM
    4. Machine learning

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    • (2024)Enhancing Breast Cancer Prediction with XAI-Enabled Boosting Algorithms2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10723843(1-5)Online publication date: 24-Jun-2024
    • (2023)Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PETJournal of Personalized Medicine10.3390/jpm1303053913:3(539)Online publication date: 17-Mar-2023

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