TY  - JOUR
TI  - Classification of Breast Cancer on the Strength of
Potential Risk Factors with Boosting Models: A Public
Health Informatics Application
AB  - 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.
AU  - ÇOLAK, Cemil
AU  - BALIKCI CICEK, IPEK
AU  - Akbulut, Sami
DO  - 10.4274/haseki.galenos.2022.8440
PY  - 2022
JO  - Haseki Tıp Bülteni
VL  - 60
IS  - 3
SN  - 1302-0072
SP  - 196
EP  - 203
DB  - TRDizin
UR  - http://search/yayin/detay/530930
ER  -