Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application
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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| Format: | Article |
| Language: | English |
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Galenos Yayinevi
2022-06-01
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| Series: | Haseki Tıp Bülteni |
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http://www.hasekidergisi.com/archives/archive-detail/article-preview/classification-of-breast-cancer-on-the-strength-of/52214
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| author | Sami Akbulut Ipek Balikci Cicek Cemil Colak |
| author_facet | Sami Akbulut Ipek Balikci Cicek Cemil Colak |
| author_sort | Sami Akbulut |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T13:52:43Z |
| format | Article |
| id | doaj.art-017dbdebab6e4e58ac4777119f9d4d2b |
| institution | Directory Open Access Journal |
| issn | 1302-0072 2147-2688 |
| language | English |
| last_indexed | 2024-04-10T13:52:43Z |
| publishDate | 2022-06-01 |
| publisher | Galenos Yayinevi |
| record_format | Article |
| series | Haseki Tıp Bülteni |
| spelling | doaj.art-017dbdebab6e4e58ac4777119f9d4d2b2023-02-15T16:10:38ZengGalenos YayineviHaseki Tıp Bülteni1302-00722147-26882022-06-0160319620310.4274/haseki.galenos.2022.844013049054Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics ApplicationSami Akbulut0Ipek Balikci Cicek1Cemil Colak2 Inonu University Faculty of Medicine, Department of General Surgery, Malatya, Turkey Inonu University Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Turkey Inonu University Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Turkey 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. http://www.hasekidergisi.com/archives/archive-detail/article-preview/classification-of-breast-cancer-on-the-strength-of/52214 breast cancerboosting algorithmgradient boosting algorithmxgboost algorithmlightgbm algorithm |
| spellingShingle | Sami Akbulut Ipek Balikci Cicek Cemil Colak Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application Haseki Tıp Bülteni breast cancer boosting algorithm gradient boosting algorithm xgboost algorithm lightgbm algorithm |
| title | Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application |
| title_full | Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application |
| title_fullStr | Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application |
| title_full_unstemmed | Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application |
| title_short | Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application |
| title_sort | classification of breast cancer on the strength of potential risk factors with boosting models a public health informatics application |
| topic | breast cancer boosting algorithm gradient boosting algorithm xgboost algorithm lightgbm algorithm |
| url |
http://www.hasekidergisi.com/archives/archive-detail/article-preview/classification-of-breast-cancer-on-the-strength-of/52214
|
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