ML: Early Breast Cancer Diagnosis
Breast cancer is the most common malignancy among women worldwide, often characterized by the uncontrolled proliferation of breast cells, leading to the formation of lumps or tumors that can be detected through medical imaging such as X-rays. Distinguishing between benign and malignant tumors presen...
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| Vydáno v: | Current problems in cancer. Case reports Ročník 13; s. 100278 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.03.2024
Elsevier |
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| ISSN: | 2666-6219, 2666-6219 |
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| Abstract | Breast cancer is the most common malignancy among women worldwide, often characterized by the uncontrolled proliferation of breast cells, leading to the formation of lumps or tumors that can be detected through medical imaging such as X-rays. Distinguishing between benign and malignant tumors presents a significant challenge in the diagnosis of breast cancer.
In this study, machine learning methods, including Logistic Regression, Gradient Boosting, Ada Boost, Random Forest, and Gaussian NB with Grid Search, were employed to differentiate between healthy individuals and those with malignancies. The results revealed that the Random Forest algorithm exhibited the highest performance in predicting breast cancer, accurately identifying 99 % of both healthy and affected individuals. Additionally, both Gradient Boosting and Ada Boost demonstrated a similar level of accuracy, correctly distinguishing 98 % of healthy and affected individuals.
Conversely, Gaussian NB performed the least effectively, with an accuracy of 91 % in differentiating between healthy and affected individuals, highlighting its comparatively lower predictive capability for breast cancer. |
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| AbstractList | AbstractBreast cancer is the most common malignancy among women worldwide, often characterized by the uncontrolled proliferation of breast cells, leading to the formation of lumps or tumors that can be detected through medical imaging such as X-rays. Distinguishing between benign and malignant tumors presents a significant challenge in the diagnosis of breast cancer. In this study, machine learning methods, including Logistic Regression, Gradient Boosting, Ada Boost, Random Forest, and Gaussian NB with Grid Search, were employed to differentiate between healthy individuals and those with malignancies. The results revealed that the Random Forest algorithm exhibited the highest performance in predicting breast cancer, accurately identifying 99% of both healthy and affected individuals. Additionally, both Gradient Boosting and Ada Boost demonstrated a similar level of accuracy, correctly distinguishing 98% of healthy and affected individuals. Conversely, Gaussian NB performed the least effectively, with an accuracy of 91% in differentiating between healthy and affected individuals, highlighting its comparatively lower predictive capability for breast cancer. Breast cancer is the most common malignancy among women worldwide, often characterized by the uncontrolled proliferation of breast cells, leading to the formation of lumps or tumors that can be detected through medical imaging such as X-rays. Distinguishing between benign and malignant tumors presents a significant challenge in the diagnosis of breast cancer.In this study, machine learning methods, including Logistic Regression, Gradient Boosting, Ada Boost, Random Forest, and Gaussian NB with Grid Search, were employed to differentiate between healthy individuals and those with malignancies. The results revealed that the Random Forest algorithm exhibited the highest performance in predicting breast cancer, accurately identifying 99 % of both healthy and affected individuals. Additionally, both Gradient Boosting and Ada Boost demonstrated a similar level of accuracy, correctly distinguishing 98 % of healthy and affected individuals.Conversely, Gaussian NB performed the least effectively, with an accuracy of 91 % in differentiating between healthy and affected individuals, highlighting its comparatively lower predictive capability for breast cancer. Breast cancer is the most common malignancy among women worldwide, often characterized by the uncontrolled proliferation of breast cells, leading to the formation of lumps or tumors that can be detected through medical imaging such as X-rays. Distinguishing between benign and malignant tumors presents a significant challenge in the diagnosis of breast cancer. In this study, machine learning methods, including Logistic Regression, Gradient Boosting, Ada Boost, Random Forest, and Gaussian NB with Grid Search, were employed to differentiate between healthy individuals and those with malignancies. The results revealed that the Random Forest algorithm exhibited the highest performance in predicting breast cancer, accurately identifying 99 % of both healthy and affected individuals. Additionally, both Gradient Boosting and Ada Boost demonstrated a similar level of accuracy, correctly distinguishing 98 % of healthy and affected individuals. Conversely, Gaussian NB performed the least effectively, with an accuracy of 91 % in differentiating between healthy and affected individuals, highlighting its comparatively lower predictive capability for breast cancer. |
| ArticleNumber | 100278 |
| Author | Suratgar, Amir Abolfazl Menhaj, Mohammad Bagher Malakouti, Seyed Matin |
| Author_xml | – sequence: 1 givenname: Seyed Matin orcidid: 0000-0003-3585-253X surname: Malakouti fullname: Malakouti, Seyed Matin email: matin.malakouti@aut.ac.ir – sequence: 2 givenname: Mohammad Bagher surname: Menhaj fullname: Menhaj, Mohammad Bagher email: menhaj@aut.ac.ir – sequence: 3 givenname: Amir Abolfazl surname: Suratgar fullname: Suratgar, Amir Abolfazl |
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| Keywords | Grid search Women worldwide Breast cancer Machine learning methods machine learning methods women worldwide Grid Search |
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