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Explainable extreme boosting model for breast cancer diagnosis

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IJECEIAES

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.

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International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5764~5769
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5764-5769  5764
Journal homepage: http://ijece.iaescore.com
Explainable extreme boosting model for breast cancer diagnosis
Tamilarasi Suresh1
, Tsehay Admassu Assegie2
, Sangeetha Ganesan3
, Ravulapalli Lakshmi Tulasi4
,
Radha Mothukuri5
, Ayodeji Olalekan Salau6
1Department of Information Technology, St. Peter’s Institute of Higher Education and Research, Chennai, India
2Department of Computer Science, College of Engineering and Technology, Injibara University, Injibara, Ethiopia
3Department of Artificial Intelligence and Data Science, RMK College of Engineering Technology, Thiruvallur District, India
4Department of Computer Science and Engineering, R.V.R and J.C. College of Engineering, Guntur, India
5Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India
6Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
Article Info ABSTRACT
Article history:
Received Sep 1, 2022
Revised Mar 13, 2023
Accepted Apr 3, 2023
This study investigates the Shapley additive explanation (SHAP) of the
extreme boosting (XGBoost) model for breast cancer diagnosis. The study
employed Wisconsin’s breast cancer dataset, characterized by 30 features
extracted from an image of a breast cell. SHAP module generated different
explainer values representing the impact of a breast cancer feature on breast
cancer diagnosis. The experiment computed SHAP values of 569 samples of
the breast cancer dataset. The SHAP explanation indicates perimeter and
concave points have the highest impact on breast cancer diagnosis. SHAP
explains the XGB model diagnosis outcome showing the features affecting
the XGBoost model. The developed XGB model achieves an accuracy of
98.42%.
Keywords:
Black-box model
Breast cancer prediction
Interpretable model
Machine learning
Model transparency This is an open access article under the CC BY-SA license.
Corresponding Author:
Tsehay Admassu Assegie
Department of Computer Science, College of Engineering and Technology, Injibara University
P.O. Box: 40, Injibara, Ethiopia
Email: tsehayadmassu2006@gmail.com
1. INTRODUCTION
Machine learning has been widely applied to the diagnosis of breast cancer. Different models, namely
extreme boosting (XGBoost) [1] and random forest (RF) [2] are applied to develop a model for breast cancer
diagnosis. The explanation of diagnosis results remained not interpretable although the XGBoost and random
forest have achieved encouraging prediction accuracy of 97% and 98.3% respectively.
Several studies applied machine-learning algorithms to breast cancer diagnosis. Dhahri et al. [3]
investigated the application of decision trees (DT), random forest, support vector machines (SVM), Gaussian
naïve Bayes, K-neighbors, and linear regression methods to breast cancer diagnosis. The study emphasized
that encouraging result is obtained with supervised learning algorithms. Adaptive boosting achieves the highest
accuracy of 97.44% on the breast cancer dataset.
Kabiraj et al. [4], studied XGBoost and random forest for analyzing the risk of breast cancer. The
simulation on the XGBoost and the random forest was conducted with 275 observations by training on
12 features. The result indicates that the random forest algorithm achieves 74.73% accuracy, while XGBoost
achieved 73.63% accuracy.
Liang et al. [5] improved the performance of XGBoost, k-nearest neighbor (KNN), Adaboost, DT,
RF, and gradient boosting decision tree (GBDT) for breast cancer diagnosis. The study tested and compared
the performance of the model for breast cancer diagnosis. The result revealed that the XGBoost scores a higher
accurateness of 90.24%, and KNN revealed the lower accuracy.
Int J Elec & Comp Eng ISSN: 2088-8708 
Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh)
5765
Derangula et al. [6] developed an optimum XGBoost model for breast cancer diagnosis. The study
applied feature selection for optimization of the XGBoost and obtained an accuracy of 96.49%. The result
shows that feature selection improves the accuracy of the XGboost model for breast cancer diagnosis. The
developed model improves the accuracy of the XGboost model presented in the previous study [5] with a
similar model by 6.09%. Hence, the feature selection significantly improves the model’s prediction accuracy
to identify the breast cancer.
Inan et al. [7], evaluated the synthetic minority oversampling technique for optimization of the
XGBoost model for breast cancer diagnosis. The study developed the XGBoost with a dataset collected from
the UCI repository. The experimental result demonstrates that the XGboost achieves a validation score of
98.4% on breast cancer identification. The validation score of 98.4% proves that XGBoost model’s
effectiveness in identifying malignant and benign cases of breast tissues. However, the study does not show a
method to intemperate the predicted result to the patient or oncologist.
Phankokkruad [8] developed a cost-sensitive XGBoost-based model for breast cancer diagnosis.
The study suggested that the cost-sensitive model achieves better accuracy compared with the non-cost-
sensitive XGBoost model. In addition, the result on breast cancer (Wisconsin) reveals that the sensitive
XGBoost model achieves 99.12% on breast cancer diagnosis. The investigation of the performance shows that
the XGBoost model achieved higher accuracy. However, the model does not explain the diagnosis result
produced.
Additionally, Vamvakas et al. [9] studied the effectiveness of the XGBoost model and improved the
diagnostic performance of the model, in differentiating breast cancer lesions. Methods: The study experimented
with 140 samples of which 70 are benign tissues and 70 malignant tissues. The XGBoost model achieved
different performances in diagnosing breast cancer. The XGboost and light gradient boosting models achieved
higher accuracy of 95% and 94% respectively. However, the study does not investigate the explain ability of
the model.
Most of the machine learning models applied to breast cancer diagnosis are black box model
[10]–[15]. Contrary to the interpretable model the black box model does not provide mechanisms to
explain how the model makes decisions in breast cancer diagnosis. Thus, the development of models that
make the behaviors and the diagnosis outcomes of a model understandable to humans requires much research
effort.
Developing a model for breast cancer diagnosis with machine learning has been effective. For
instance, several studies [16]–[19] have suggested that the use of a machine learning model for breast cancer
diagnosis is encouraging. The machine-learning model achieved 97.77%. While the 97.77% accuracy shows
encouraging achievement, the prediction outcome explanation and the interpretation of the internal working of
the supervised model requires much research effort to develop a practical trustable and transparent model for
the diagnosis of breast cancer.
Recently, different researchers have proposed methods for explaining complex models for breast
cancer diagnosis. As a result, several approaches exist to explain the prediction outcome of the
machine-learning model. One of the approaches for explaining the prediction outcome of a machine learning
model for classification problems such as breast cancer diagnosis is the Shapley additive explanation (SHAP)
[20]–[22].
This research aims to develop an XGBoost model with SHAP to explain the output for breast cancer
diagnosis. The study is motivated to investigate the interpretability of XGBoost model output with SHAP. The
objective of the study includes the following: i) to study the relation between breast cancer features and target
labels with SHAP; ii) to investigate how the XGBoost model is making decisions in breast cancer diagnosis;
iii) to analyze the features that have an impact on the XGBoost model decision-making process. The
organization of the rest of the study is as follows: section 2 discusses the method, section 3 presents the result,
and section 4 provides the conclusion, and discusses the implication of the result obtained.
2. METHOD
This research employed the University of California Irvine (UCI) dataset for experimental
analysis. Several studies [23]–[25] have employed the UCI dataset for the development of machine learning
models for breast cancer diagnosis. An open-source scientific learning kit (sci-kit-learn) was employed to
develop the XGBoost model. The 569 samples collected from the UCI data repository were split into
a training set (70% of the total sample) and a testing set (30% of the sample). Then, the model is tested on the
testing set and the prediction result is explained by SHAP values produced by the force plot for the samples
used in the experiment. Figure 1 indicates the research chronology and the procedures employed in this
research.
 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 5764-5769
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Figure 1. Explainable XGBoost model for breast cancer diagnosis
3. RESULTS AND DISCUSSION
This section presents the result of the SHAP explanation for the XGBoost model prediction output for
benign and malignant samples of the training set. The explanation produced by SHAP is analyzed in three
experiments. The first two experiments presented in sections 3.1 and 3.2 show the local explanation for benign
and malign tissue in the test set. The third experiment shows the global explanations indicating the effect of
each feature on the XGBoost model outcome as revealed in section 3.3.
3.1. SHAP explanation for benign tissue
Figure 2 shows SHAP values for the benign tissue, which is the first in the training set. The SHAP
values are beneficial for interpreting individual results of the XGBoost model output for breast cancer
diagnosis. The explanation provided by the SHAP values lowers the generalization of the XGBoost model to
a single prediction that makes the interpretation intuitive, resulting in explanations that are more useful. The
individual prediction result of the XGBoost model is produced with force plots, the horizontal axis showing
the SHAP value of the final prediction, and each feature’s contribution is shown in a block: left of the result,
the positive SHAP values (forcing the probability higher) and lower SHAP values in the right (forcing the
probability lower). We can see how the final SHAP value (which can be transformed from a continuous domain
to probability using a sigmoid) is 2.29 and higher than the base value, therefore predicted as benign. The
representation shows how, despite the texture having anomalous values, the concavity and perimeter feature
finally turn the prediction to the positive side. The highest contribution is the one by concave point worst.
Figure 2. SHAP explanation of XGBoost model output for benign tissue
3.2. SHAP explanation for malignant tissue
The experiment considered malignant tissue to analyze the explanation provided by the SHAP values
on the positive prediction outcome of the XGBoost model. The result of the SHAP explanation for the malign
tissue is shown in Figure 3. As shown in Figure 3, the concavity, texture, and perimeter of the cells make the
model predict it as a negative case. This model can make accurate predictions, but thanks to these SHAP
interpretations, also its explanations are available, which provides practitioners with useful information to
understand the model.
Int J Elec & Comp Eng ISSN: 2088-8708 
Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh)
5767
Figure 3. SHAP explanation of XGBoost model output for malign tissue
3.3. SHAP explanation for malign and benign tissue
The experiment considered malignant tissue to analyze the explanation provided by the SHAP values
on the positive prediction outcome of the XGBoost model. The result of the SHAP explanation for the malign
tissue is indicated in Figure 4. As shown in Figure 4, the concavity, texture, and perimeter of the cells forced,
the XGBoost model predict it as a negative case.
Figure 4. Feature impact on XGBoost model output generated by SHAP
4. CONCLUSION
This study investigated the effectiveness of SHAP in explaining the XGBoost-based model for breast
cancer diagnosis. Moreover, the study developed an explainable XGBoost-based model that assists in the
diagnosis of breast cancer as a preliminary examination. The study also suggests that SHAP explains the
diagnostic outcome of the XGboost model with a forced plot, providing insights into the impact of each breast
cancer feature on the XGBoost model output. Furthermore, the explanation XGBoost model provided by SHAP
is vital to develop an explainable model to diagnose breast cancer. The developed XGBoost model for breast
cancer diagnosis achieves 98.42% accuracy. Additionally, the SHAP explanation provides an interpretation of
XGBoost model diagnosis outcomes. The study recommends the investigation of the explanation of other
machine learning models such as support vector machine, random forest, and deep learning to develop an
explainable model for breast cancer diagnosis in the future work.
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BIOGRAPHIES OF AUTHORS
Tamilarasi Suresh is heading the department of Information Technology at St.
Peter’s Institute of Higher Education and Research. Her broad areas of interest in research are
Machine learning, software engineering, artificial intelligence and cloud computing. Her journey
in SPIHER had started 2 years ago and it has been a wonderful environment to nurture her
academic capabilities. She can be contacted at email: tamilarasisu@gmail.com.
Int J Elec & Comp Eng ISSN: 2088-8708 
Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh)
5769
Tsehay Admassu Assegie received his M.Sc., in Computer Science from Andhra
University, India 2016. He received his B.Sc. in Computer Science from Dilla University,
Ethiopia, in 2013. He is currently working as a lecturer in the Department of Computer Science,
College of Engineering and Technology, Injibara University, Injibara, Ethiopia. His research
includes machine learning, the application of machine learning in healthcare, network security,
and software-defined networking. His research has been published in many reputable
international journals, and international conferences. He is a member of the International
Association of Engineers (IAENG). He has reviewed many papers published in different
scientific journals. He is an active reviewer of different reputed journals. Recently, Web of
Science has verified 8 peer reviews by him, published in multi-disciplinary digital publishing
institute (MDPI) journals. He can be contacted at email: tsehayadmassu2006@gmail.com.
Sangeetha Ganesan received her B.E degree in Computer Science and Engineering
from Periyar University Salem, M.E degree in Computer Science and Engineering from Anna
University, Chennai, and Ph.D., in Faculty of Information and Communication Engineering,
Anna University, Chennai. She is currently working as an Associate Professor in R.M.K. College
of Engineering and Technology, Puduvoyal. Her research interests are distributed computing,
cloud computing, security, data science and machine learning. She can be contacted at email:
sangeethaads@rmkcet.ac.in.
Ravulapalli Lakshmi Tulasi is currently working as a Professor in the Department
of Computer Science and Engineering, R.V.R and J.C College of Engineering, Guntur, Andhra
Pradesh, India. Her research interests include machine learning, data mining, information
retrieval systems, and semantic Web. She can be contacted at email: rtulasi.2002@gmail.com.
Radha Mothukuri is currently working as an Associate professor in the department
of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation,
Vaddeswaram, Andhra Pradesh, India. She is awarded Ph.D in Computer Science and
Engineering from Acharya Nagarjuna University. Her area of interest is text mining, machine
learning, and cloud computing, network security. She can be contacted at email:
radhahemanth12@gmail.com.
Ayodeji Olalekan Salau received the B.Eng. in Electrical/Computer Engineering
from the Federal University of Technology, Minna, Nigeria. He received the M.Sc. and Ph.D.
degrees from the Obafemi Awolowo University, Ile-Ife, Nigeria. His research interests include
research in the fields of computer vision, image processing, signal processing, machine learning,
control systems engineering and power systems technology. Dr. Salau serves as a reviewer for
several reputable international journals. His research has been published in many reputable
international conferences, books, and major international journals. He is a registered Engineer
with the Council for the Regulation of Engineering in Nigeria (COREN), a member of the
International Association of Engineers (IAENG), and a recipient of the Quarterly Franklin
Membership with ID number CR32878 given by the Editorial Board of London Journals Press
in 2020 for top quality research output. More recently, Dr. Salau’s research paper was awarded
the best paper of the year 2019 in Cogent Engineering. In addition, he is the recipient of the
International Research Award on New Science Inventions (NESIN) under the category of “Best
Researcher Award” given by Science Father with ID number 9249, 2020. Currently, Dr. Salau
works at Afe Babalola University in the Department of Electrical/Electronics and Computer
Engineering. He can be contacted at email: ayodejisalau98@gmai.com.

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Explainable extreme boosting model for breast cancer diagnosis

  • 1. International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 5, October 2023, pp. 5764~5769 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5764-5769  5764 Journal homepage: http://ijece.iaescore.com Explainable extreme boosting model for breast cancer diagnosis Tamilarasi Suresh1 , Tsehay Admassu Assegie2 , Sangeetha Ganesan3 , Ravulapalli Lakshmi Tulasi4 , Radha Mothukuri5 , Ayodeji Olalekan Salau6 1Department of Information Technology, St. Peter’s Institute of Higher Education and Research, Chennai, India 2Department of Computer Science, College of Engineering and Technology, Injibara University, Injibara, Ethiopia 3Department of Artificial Intelligence and Data Science, RMK College of Engineering Technology, Thiruvallur District, India 4Department of Computer Science and Engineering, R.V.R and J.C. College of Engineering, Guntur, India 5Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India 6Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria Article Info ABSTRACT Article history: Received Sep 1, 2022 Revised Mar 13, 2023 Accepted Apr 3, 2023 This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%. Keywords: Black-box model Breast cancer prediction Interpretable model Machine learning Model transparency This is an open access article under the CC BY-SA license. Corresponding Author: Tsehay Admassu Assegie Department of Computer Science, College of Engineering and Technology, Injibara University P.O. Box: 40, Injibara, Ethiopia Email: tsehayadmassu2006@gmail.com 1. INTRODUCTION Machine learning has been widely applied to the diagnosis of breast cancer. Different models, namely extreme boosting (XGBoost) [1] and random forest (RF) [2] are applied to develop a model for breast cancer diagnosis. The explanation of diagnosis results remained not interpretable although the XGBoost and random forest have achieved encouraging prediction accuracy of 97% and 98.3% respectively. Several studies applied machine-learning algorithms to breast cancer diagnosis. Dhahri et al. [3] investigated the application of decision trees (DT), random forest, support vector machines (SVM), Gaussian naïve Bayes, K-neighbors, and linear regression methods to breast cancer diagnosis. The study emphasized that encouraging result is obtained with supervised learning algorithms. Adaptive boosting achieves the highest accuracy of 97.44% on the breast cancer dataset. Kabiraj et al. [4], studied XGBoost and random forest for analyzing the risk of breast cancer. The simulation on the XGBoost and the random forest was conducted with 275 observations by training on 12 features. The result indicates that the random forest algorithm achieves 74.73% accuracy, while XGBoost achieved 73.63% accuracy. Liang et al. [5] improved the performance of XGBoost, k-nearest neighbor (KNN), Adaboost, DT, RF, and gradient boosting decision tree (GBDT) for breast cancer diagnosis. The study tested and compared the performance of the model for breast cancer diagnosis. The result revealed that the XGBoost scores a higher accurateness of 90.24%, and KNN revealed the lower accuracy.
  • 2. Int J Elec & Comp Eng ISSN: 2088-8708  Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh) 5765 Derangula et al. [6] developed an optimum XGBoost model for breast cancer diagnosis. The study applied feature selection for optimization of the XGBoost and obtained an accuracy of 96.49%. The result shows that feature selection improves the accuracy of the XGboost model for breast cancer diagnosis. The developed model improves the accuracy of the XGboost model presented in the previous study [5] with a similar model by 6.09%. Hence, the feature selection significantly improves the model’s prediction accuracy to identify the breast cancer. Inan et al. [7], evaluated the synthetic minority oversampling technique for optimization of the XGBoost model for breast cancer diagnosis. The study developed the XGBoost with a dataset collected from the UCI repository. The experimental result demonstrates that the XGboost achieves a validation score of 98.4% on breast cancer identification. The validation score of 98.4% proves that XGBoost model’s effectiveness in identifying malignant and benign cases of breast tissues. However, the study does not show a method to intemperate the predicted result to the patient or oncologist. Phankokkruad [8] developed a cost-sensitive XGBoost-based model for breast cancer diagnosis. The study suggested that the cost-sensitive model achieves better accuracy compared with the non-cost- sensitive XGBoost model. In addition, the result on breast cancer (Wisconsin) reveals that the sensitive XGBoost model achieves 99.12% on breast cancer diagnosis. The investigation of the performance shows that the XGBoost model achieved higher accuracy. However, the model does not explain the diagnosis result produced. Additionally, Vamvakas et al. [9] studied the effectiveness of the XGBoost model and improved the diagnostic performance of the model, in differentiating breast cancer lesions. Methods: The study experimented with 140 samples of which 70 are benign tissues and 70 malignant tissues. The XGBoost model achieved different performances in diagnosing breast cancer. The XGboost and light gradient boosting models achieved higher accuracy of 95% and 94% respectively. However, the study does not investigate the explain ability of the model. Most of the machine learning models applied to breast cancer diagnosis are black box model [10]–[15]. Contrary to the interpretable model the black box model does not provide mechanisms to explain how the model makes decisions in breast cancer diagnosis. Thus, the development of models that make the behaviors and the diagnosis outcomes of a model understandable to humans requires much research effort. Developing a model for breast cancer diagnosis with machine learning has been effective. For instance, several studies [16]–[19] have suggested that the use of a machine learning model for breast cancer diagnosis is encouraging. The machine-learning model achieved 97.77%. While the 97.77% accuracy shows encouraging achievement, the prediction outcome explanation and the interpretation of the internal working of the supervised model requires much research effort to develop a practical trustable and transparent model for the diagnosis of breast cancer. Recently, different researchers have proposed methods for explaining complex models for breast cancer diagnosis. As a result, several approaches exist to explain the prediction outcome of the machine-learning model. One of the approaches for explaining the prediction outcome of a machine learning model for classification problems such as breast cancer diagnosis is the Shapley additive explanation (SHAP) [20]–[22]. This research aims to develop an XGBoost model with SHAP to explain the output for breast cancer diagnosis. The study is motivated to investigate the interpretability of XGBoost model output with SHAP. The objective of the study includes the following: i) to study the relation between breast cancer features and target labels with SHAP; ii) to investigate how the XGBoost model is making decisions in breast cancer diagnosis; iii) to analyze the features that have an impact on the XGBoost model decision-making process. The organization of the rest of the study is as follows: section 2 discusses the method, section 3 presents the result, and section 4 provides the conclusion, and discusses the implication of the result obtained. 2. METHOD This research employed the University of California Irvine (UCI) dataset for experimental analysis. Several studies [23]–[25] have employed the UCI dataset for the development of machine learning models for breast cancer diagnosis. An open-source scientific learning kit (sci-kit-learn) was employed to develop the XGBoost model. The 569 samples collected from the UCI data repository were split into a training set (70% of the total sample) and a testing set (30% of the sample). Then, the model is tested on the testing set and the prediction result is explained by SHAP values produced by the force plot for the samples used in the experiment. Figure 1 indicates the research chronology and the procedures employed in this research.
  • 3.  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 5764-5769 5766 Figure 1. Explainable XGBoost model for breast cancer diagnosis 3. RESULTS AND DISCUSSION This section presents the result of the SHAP explanation for the XGBoost model prediction output for benign and malignant samples of the training set. The explanation produced by SHAP is analyzed in three experiments. The first two experiments presented in sections 3.1 and 3.2 show the local explanation for benign and malign tissue in the test set. The third experiment shows the global explanations indicating the effect of each feature on the XGBoost model outcome as revealed in section 3.3. 3.1. SHAP explanation for benign tissue Figure 2 shows SHAP values for the benign tissue, which is the first in the training set. The SHAP values are beneficial for interpreting individual results of the XGBoost model output for breast cancer diagnosis. The explanation provided by the SHAP values lowers the generalization of the XGBoost model to a single prediction that makes the interpretation intuitive, resulting in explanations that are more useful. The individual prediction result of the XGBoost model is produced with force plots, the horizontal axis showing the SHAP value of the final prediction, and each feature’s contribution is shown in a block: left of the result, the positive SHAP values (forcing the probability higher) and lower SHAP values in the right (forcing the probability lower). We can see how the final SHAP value (which can be transformed from a continuous domain to probability using a sigmoid) is 2.29 and higher than the base value, therefore predicted as benign. The representation shows how, despite the texture having anomalous values, the concavity and perimeter feature finally turn the prediction to the positive side. The highest contribution is the one by concave point worst. Figure 2. SHAP explanation of XGBoost model output for benign tissue 3.2. SHAP explanation for malignant tissue The experiment considered malignant tissue to analyze the explanation provided by the SHAP values on the positive prediction outcome of the XGBoost model. The result of the SHAP explanation for the malign tissue is shown in Figure 3. As shown in Figure 3, the concavity, texture, and perimeter of the cells make the model predict it as a negative case. This model can make accurate predictions, but thanks to these SHAP interpretations, also its explanations are available, which provides practitioners with useful information to understand the model.
  • 4. Int J Elec & Comp Eng ISSN: 2088-8708  Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh) 5767 Figure 3. SHAP explanation of XGBoost model output for malign tissue 3.3. SHAP explanation for malign and benign tissue The experiment considered malignant tissue to analyze the explanation provided by the SHAP values on the positive prediction outcome of the XGBoost model. The result of the SHAP explanation for the malign tissue is indicated in Figure 4. As shown in Figure 4, the concavity, texture, and perimeter of the cells forced, the XGBoost model predict it as a negative case. Figure 4. Feature impact on XGBoost model output generated by SHAP 4. CONCLUSION This study investigated the effectiveness of SHAP in explaining the XGBoost-based model for breast cancer diagnosis. Moreover, the study developed an explainable XGBoost-based model that assists in the diagnosis of breast cancer as a preliminary examination. The study also suggests that SHAP explains the diagnostic outcome of the XGboost model with a forced plot, providing insights into the impact of each breast cancer feature on the XGBoost model output. Furthermore, the explanation XGBoost model provided by SHAP is vital to develop an explainable model to diagnose breast cancer. The developed XGBoost model for breast cancer diagnosis achieves 98.42% accuracy. Additionally, the SHAP explanation provides an interpretation of XGBoost model diagnosis outcomes. The study recommends the investigation of the explanation of other machine learning models such as support vector machine, random forest, and deep learning to develop an explainable model for breast cancer diagnosis in the future work. REFERENCES [1] X. Y. Liew, N. Hameed, and J. Clos, “An investigation of XGBoost-based algorithm for breast cancer classification,” Machine Learning with Applications, vol. 6, Dec. 2021, doi: 10.1016/j.mlwa.2021.100154. [2] T. A. Assegie, R. L. Tulasi, V. Elanangai, and N. K. Kumar, “Exploring the performance of feature selection method using breast cancer dataset,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 25, no. 1, pp. 232–237, Jan. 2022, doi: 10.11591/ijeecs.v25.i1.pp232-237.
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Mhammedi, H. Ghandi, F. Qanouni, and M. Bahaj, “An ontological model based on machine learning for predicting breast cancer,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, 2022, doi: 10.14569/IJACSA.2022.0130715. [14] M. N. Haque et al., “Predicting characteristics associated with breast cancer survival using multiple machine learning approaches,” Computational and Mathematical Methods in Medicine, vol. 2022, pp. 1–12, Apr. 2022, doi: 10.1155/2022/1249692. [15] C. Hou et al., “Predicting breast cancer in Chinese women using machine learning techniques: algorithm development,” JMIR Medical Informatics, vol. 8, no. 6, Jun. 2020, doi: 10.2196/17364. [16] W. Wu and S. Faisal, “A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application,” Transactions of the Institute of Measurement and Control, vol. 42, no. 7, pp. 1301–1312, Apr. 2020, doi: 10.1177/0142331219889221. [17] M. M. Islam, H. Iqbal, M. R. Haque, and M. K. Hasan, “Prediction of breast cancer using support vector machine and K-nearest neighbors,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dec. 2017, pp. 226–229, doi: 10.1109/R10- HTC.2017.8288944. [18] M. M. Islam, M. R. Haque, H. Iqbal, M. M. Hasan, M. Hasan, and M. N. Kabir, “Breast cancer prediction: A comparative study using machine learning techniques,” SN Computer Science, vol. 1, no. 5, Sep. 2020, doi: 10.1007/s42979-020-00305-w. [19] D. Singh and M. Singh, “Classification of mammograms using support vector machine,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 5, pp. 259–268, May 2016, doi: 10.14257/ijsip.2016.9.5.23. [20] T. A. Assegie, “Evaluation of the Shapley additive explanation technique for ensemble learning methods,” Proceedings of Engineering and Technology Innovation, vol. 21, pp. 20–26, Apr. 2022, doi: 10.46604/peti.2022.9025. [21] A. Moncada-Torres, M. C. van Maaren, M. P. Hendriks, S. Siesling, and G. Geleijnse, “Explainable machine learning can outperform cox regression predictions and provide insights in breast cancer survival,” Scientific Reports, vol. 11, no. 1, Mar. 2021, doi: 10.1038/s41598-021-86327-7. [22] M. R. Zafar and N. M. Khan, “DLIME: A deterministic local interpretable model-agnostic explanations approach for computer- aided diagnosis systems,” Prepr. arXiv.1906.10263, Jun. 2019. [23] H. Hakkoum, A. Idri, and I. Abnane, “Artificial neural networks interpretation using LIME for breast cancer diagnosis,” in WorldCIST 2020: Trends and Innovations in Information Systems and Technologies, 2020, pp. 15–24, doi: 10.1007/978-3-030- 45697-9_2. [24] T. A. Assegie and S. J. Sushma, “A support vector machine and decision tree based breast cancer prediction,” International Journal of Engineering and Advanced Technology, vol. 9, no. 3, pp. 2972–2976, Feb. 2020, doi: 10.35940/ijeat.A1752.029320. [25] Y. Zhang, Y. Weng, and J. Lund, “Applications of explainable artificial intelligence in diagnosis and surgery,” Diagnostics, vol. 12, no. 2, Jan. 2022, doi: 10.3390/diagnostics12020237. BIOGRAPHIES OF AUTHORS Tamilarasi Suresh is heading the department of Information Technology at St. Peter’s Institute of Higher Education and Research. Her broad areas of interest in research are Machine learning, software engineering, artificial intelligence and cloud computing. Her journey in SPIHER had started 2 years ago and it has been a wonderful environment to nurture her academic capabilities. She can be contacted at email: tamilarasisu@gmail.com.
  • 6. Int J Elec & Comp Eng ISSN: 2088-8708  Explainable extreme boosting model for breast cancer diagnosis (Tamilarasi Suresh) 5769 Tsehay Admassu Assegie received his M.Sc., in Computer Science from Andhra University, India 2016. He received his B.Sc. in Computer Science from Dilla University, Ethiopia, in 2013. He is currently working as a lecturer in the Department of Computer Science, College of Engineering and Technology, Injibara University, Injibara, Ethiopia. His research includes machine learning, the application of machine learning in healthcare, network security, and software-defined networking. His research has been published in many reputable international journals, and international conferences. He is a member of the International Association of Engineers (IAENG). He has reviewed many papers published in different scientific journals. He is an active reviewer of different reputed journals. Recently, Web of Science has verified 8 peer reviews by him, published in multi-disciplinary digital publishing institute (MDPI) journals. He can be contacted at email: tsehayadmassu2006@gmail.com. Sangeetha Ganesan received her B.E degree in Computer Science and Engineering from Periyar University Salem, M.E degree in Computer Science and Engineering from Anna University, Chennai, and Ph.D., in Faculty of Information and Communication Engineering, Anna University, Chennai. She is currently working as an Associate Professor in R.M.K. College of Engineering and Technology, Puduvoyal. Her research interests are distributed computing, cloud computing, security, data science and machine learning. She can be contacted at email: sangeethaads@rmkcet.ac.in. Ravulapalli Lakshmi Tulasi is currently working as a Professor in the Department of Computer Science and Engineering, R.V.R and J.C College of Engineering, Guntur, Andhra Pradesh, India. Her research interests include machine learning, data mining, information retrieval systems, and semantic Web. She can be contacted at email: rtulasi.2002@gmail.com. Radha Mothukuri is currently working as an Associate professor in the department of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. She is awarded Ph.D in Computer Science and Engineering from Acharya Nagarjuna University. Her area of interest is text mining, machine learning, and cloud computing, network security. She can be contacted at email: radhahemanth12@gmail.com. Ayodeji Olalekan Salau received the B.Eng. in Electrical/Computer Engineering from the Federal University of Technology, Minna, Nigeria. He received the M.Sc. and Ph.D. degrees from the Obafemi Awolowo University, Ile-Ife, Nigeria. His research interests include research in the fields of computer vision, image processing, signal processing, machine learning, control systems engineering and power systems technology. Dr. Salau serves as a reviewer for several reputable international journals. His research has been published in many reputable international conferences, books, and major international journals. He is a registered Engineer with the Council for the Regulation of Engineering in Nigeria (COREN), a member of the International Association of Engineers (IAENG), and a recipient of the Quarterly Franklin Membership with ID number CR32878 given by the Editorial Board of London Journals Press in 2020 for top quality research output. More recently, Dr. Salau’s research paper was awarded the best paper of the year 2019 in Cogent Engineering. In addition, he is the recipient of the International Research Award on New Science Inventions (NESIN) under the category of “Best Researcher Award” given by Science Father with ID number 9249, 2020. Currently, Dr. Salau works at Afe Babalola University in the Department of Electrical/Electronics and Computer Engineering. He can be contacted at email: ayodejisalau98@gmai.com.