Machine learning-based diagnosis of breast cancer utilizing feature optimization technique
Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recup...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990023000071 |
| _version_ | 1797802749257056256 |
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| author | Khandaker Mohammad Mohi Uddin Nitish Biswas Sarreha Tasmin Rikta Samrat Kumar Dey |
| author_facet | Khandaker Mohammad Mohi Uddin Nitish Biswas Sarreha Tasmin Rikta Samrat Kumar Dey |
| author_sort | Khandaker Mohammad Mohi Uddin |
| collection | DOAJ |
| description | Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react. |
| first_indexed | 2024-03-13T05:10:25Z |
| format | Article |
| id | doaj.art-df6154838442428f9c4b160a15a094fe |
| institution | Directory Open Access Journal |
| issn | 2666-9900 |
| language | English |
| last_indexed | 2024-03-13T05:10:25Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj.art-df6154838442428f9c4b160a15a094fe2023-06-16T05:12:12ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002023-01-013100098Machine learning-based diagnosis of breast cancer utilizing feature optimization techniqueKhandaker Mohammad Mohi Uddin0Nitish Biswas1Sarreha Tasmin Rikta2Samrat Kumar Dey3Department of Computer Science and Engineering, Dhaka International University, Dhaka, 1205, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Dhaka International University, Dhaka, 1205, BangladeshDepartment of Computer Science and Engineering, Dhaka International University, Dhaka, 1205, BangladeshSchool of Science and Technology, Bangladesh Open University, Gazipur, 1705, BangladeshBreast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.http://www.sciencedirect.com/science/article/pii/S2666990023000071Breast cancerFeature optimizationDiagnosisWBCDVoting classifierWeb application |
| spellingShingle | Khandaker Mohammad Mohi Uddin Nitish Biswas Sarreha Tasmin Rikta Samrat Kumar Dey Machine learning-based diagnosis of breast cancer utilizing feature optimization technique Computer Methods and Programs in Biomedicine Update Breast cancer Feature optimization Diagnosis WBCD Voting classifier Web application |
| title | Machine learning-based diagnosis of breast cancer utilizing feature optimization technique |
| title_full | Machine learning-based diagnosis of breast cancer utilizing feature optimization technique |
| title_fullStr | Machine learning-based diagnosis of breast cancer utilizing feature optimization technique |
| title_full_unstemmed | Machine learning-based diagnosis of breast cancer utilizing feature optimization technique |
| title_short | Machine learning-based diagnosis of breast cancer utilizing feature optimization technique |
| title_sort | machine learning based diagnosis of breast cancer utilizing feature optimization technique |
| topic | Breast cancer Feature optimization Diagnosis WBCD Voting classifier Web application |
| url | http://www.sciencedirect.com/science/article/pii/S2666990023000071 |
| work_keys_str_mv | AT khandakermohammadmohiuddin machinelearningbaseddiagnosisofbreastcancerutilizingfeatureoptimizationtechnique AT nitishbiswas machinelearningbaseddiagnosisofbreastcancerutilizingfeatureoptimizationtechnique AT sarrehatasminrikta machinelearningbaseddiagnosisofbreastcancerutilizingfeatureoptimizationtechnique AT samratkumardey machinelearningbaseddiagnosisofbreastcancerutilizingfeatureoptimizationtechnique |