Classification of Breast Cancer on the Strength of Potential Risk

Aim: The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied...

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Main Authors: Akbulut, S, Cicek, IB, Colak, C
Language:unknown
Published: 2022
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Online Access:http://hdl.handle.net/11616/86183
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spelling ftinonuuniv:oai:abakus.inonu.edu.tr:11616/86183 2023-05-15T18:11:52+02:00 Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application Akbulut, S Cicek, IB Colak, C 2022 http://hdl.handle.net/11616/86183 unknown http://hdl.handle.net/11616/86183 HASEKI TIP BULTENI-MEDICAL BULLETIN OF HASEKI 2022 ftinonuuniv 2023-01-05T18:01:48Z Aim: The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared. Methods: The open-access Breast Cancer Wisconsin Dataset, which includes 10 features of breast tumors and results from 569 patients, was used for this study. The GBM, XGBoost, and LightGBM models for classifying breast cancer were established by a repeated stratified K-fold cross validation method. The performance of the model was evaluated with accuracy, recall, precision, and area under the curve (AUC). Results: Accuracy, recall, AUC, and precision values obtained from the GBM, XGBoost, and LightGBM models were as follows: (93.9%, 93.5%, 0.984, 93.8%), (94.6%, 94%, 0.985, 94.6%), and (95.3%, 94.8%, 0.987, 95.5%), respectively. According to these results, the best performance metrics were obtained from the LightGBM model. When the effects of the variables in the dataset on breast cancer were assessed in this study, the five most significant factors for the LightGBM model were the mean of concave points, texture mean, concavity mean, radius mean, and perimeter mean, respectively. Conclusion: According to the findings obtained from the study, the LightGBM model gave more successful predictions for breast cancer classification compared with other models. Unlike similar studies examining the same dataset, this study presented variable significance for breast cancer-related variables. Applying the LightGBM approach in the medical field can help doctors make a quick and precise diagnosis. C1 [Akbulut, Sami] Inonu Univ, Fac Med, Dept Gen Surg, Malatya, Turkey. [Akbulut, Sami; Cicek, Ipek Balikci; Colak, Cemil] Inonu Univ, Fac Med, Dept Biostat & Med Informat, Malatya, Turkey. C3 Inonu University; Inonu University Other/Unknown Material sami Unknown
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language unknown
description Aim: The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared. Methods: The open-access Breast Cancer Wisconsin Dataset, which includes 10 features of breast tumors and results from 569 patients, was used for this study. The GBM, XGBoost, and LightGBM models for classifying breast cancer were established by a repeated stratified K-fold cross validation method. The performance of the model was evaluated with accuracy, recall, precision, and area under the curve (AUC). Results: Accuracy, recall, AUC, and precision values obtained from the GBM, XGBoost, and LightGBM models were as follows: (93.9%, 93.5%, 0.984, 93.8%), (94.6%, 94%, 0.985, 94.6%), and (95.3%, 94.8%, 0.987, 95.5%), respectively. According to these results, the best performance metrics were obtained from the LightGBM model. When the effects of the variables in the dataset on breast cancer were assessed in this study, the five most significant factors for the LightGBM model were the mean of concave points, texture mean, concavity mean, radius mean, and perimeter mean, respectively. Conclusion: According to the findings obtained from the study, the LightGBM model gave more successful predictions for breast cancer classification compared with other models. Unlike similar studies examining the same dataset, this study presented variable significance for breast cancer-related variables. Applying the LightGBM approach in the medical field can help doctors make a quick and precise diagnosis. C1 [Akbulut, Sami] Inonu Univ, Fac Med, Dept Gen Surg, Malatya, Turkey. [Akbulut, Sami; Cicek, Ipek Balikci; Colak, Cemil] Inonu Univ, Fac Med, Dept Biostat & Med Informat, Malatya, Turkey. C3 Inonu University; Inonu University
author Akbulut, S
Cicek, IB
Colak, C
spellingShingle Akbulut, S
Cicek, IB
Colak, C
Classification of Breast Cancer on the Strength of Potential Risk
author_facet Akbulut, S
Cicek, IB
Colak, C
author_sort Akbulut, S
title Classification of Breast Cancer on the Strength of Potential Risk
title_short Classification of Breast Cancer on the Strength of Potential Risk
title_full Classification of Breast Cancer on the Strength of Potential Risk
title_fullStr Classification of Breast Cancer on the Strength of Potential Risk
title_full_unstemmed Classification of Breast Cancer on the Strength of Potential Risk
title_sort classification of breast cancer on the strength of potential risk
publishDate 2022
url http://hdl.handle.net/11616/86183
genre sami
genre_facet sami
op_source HASEKI TIP BULTENI-MEDICAL BULLETIN OF HASEKI
op_relation http://hdl.handle.net/11616/86183
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