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Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

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@article{IJASEIT12955,
   author = {Mohanad A. Deif and Rania E. Hammam and Ahmed A. A. Solyman},
   title = {Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {508--515},
   keywords = {Risk predictive model; chronic myeloid leukemia; breast cancer; gradient boosting machine; classification algorithm.},
   abstract = {The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12955},
   doi = {10.18517/ijaseit.11.2.12955}
}

EndNote

%A A. Deif, Mohanad
%A E. Hammam, Rania
%A Solyman, Ahmed A. A.
%D 2021
%T Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis
%B 2021
%9 Risk predictive model; chronic myeloid leukemia; breast cancer; gradient boosting machine; classification algorithm.
%! Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis
%K Risk predictive model; chronic myeloid leukemia; breast cancer; gradient boosting machine; classification algorithm.
%X The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12955
%R doi:10.18517/ijaseit.11.2.12955
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 2
%@ 2088-5334

IEEE

Mohanad A. Deif,Rania E. Hammam and Ahmed A. A. Solyman,"Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 508-515, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.12955.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - A. Deif, Mohanad
AU  - E. Hammam, Rania
AU  - Solyman, Ahmed A. A.
PY  - 2021
TI  - Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 508
EP  - 515
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Risk predictive model; chronic myeloid leukemia; breast cancer; gradient boosting machine; classification algorithm.
N2  - The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12955
DO  - 10.18517/ijaseit.11.2.12955

RefWorks

RT Journal Article
ID 12955
A1 A. Deif, Mohanad
A1 E. Hammam, Rania
A1 Solyman, Ahmed A. A.
T1 Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 508
OP 515
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Risk predictive model; chronic myeloid leukemia; breast cancer; gradient boosting machine; classification algorithm.
AB The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12955
DO  - 10.18517/ijaseit.11.2.12955