BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS | Kartikasari | MEDIA STATISTIKA

BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS

*Puspita Kartikasari orcid scopus  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Iut Tri Utami  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Suparti Suparti  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Syair Dafiq Faizur Rahman  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
This study examines the existence of breast cancer from the perspective of statistics as one alternative solution. From a statistical point of view, breast cancer management can be done with early detection and appropriate and fast treatment measures through diagnosis classification. In conducting early detection, an accurate diagnosis model is needed and can be developed by developing and testing statistical methods, one of which is the classification method. The classification methods used in this study are Support Vector Machine (SVM) and LightGBM. Both methods have a high level of classification accuracy because the algorithm used is robust and sensitive in determining each object in the classification member. Therefore, these two methods classify breast cancer into malignant and benign categories. The results of this study show that the best method to classify breast cancer is the SVM method, with an accuracy rate of 97.9%.
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Keywords: Classification, Breast Cancer, SVM, LightGBM
Funding: LPPM Universitas Diponegoro scheme Riset Pengembangan dan Penerapan (RPP), number of contract 609-16/UN7.D2/PP/VIII/2023.
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Section: Articles
Language : EN
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