Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

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Product Code: Python - Machine Learning
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Product Description

Aim:

          The primary aim of this study is to develop a robust and accurate auxiliary diagnostic system for breast cancer by integrating machine learning techniques with a hybrid strategy.


Abstract:

            Breast cancer has replaced lung cancer as the number one cancer among women worldwide. In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. First, the combined sampling method SMOTE-ENN is used to solve the problem of sample imbalance, and the data are standardized to make the data have better separability. Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the Logistic Regression algorithm. Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. Finally, four different machine learning models are used for classification prediction, the best combination of parameters for each model is found, and the final results of each model are derived. The experiments are conducted using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), and the results of the study find that after the data are processed by the hybrid strategy, the best prediction results are obtained using the Random Forest model with high accuracy, which is better than the previous research methods.


Introduction:

          According to the Cancer Statistics, 2023 statistical estimates, breast cancer, lung cancer, and CRC account for 52% of all new diagnoses, with breast cancer alone accounting for 31% of female cancers. Breast cancer, as one of the common malignant tumors in women, has become a focus of public health attention around the world. Its early diagnosis is important for the success of treatment and patient survival. With the rapid development of machine learning and other technologies, more and more research has been devoted to applying these advanced technologies to the diagnosis and assisted decision making of breast cancer.

        Machine learning, as an important artificial intelligence technology, has the ability to extract features, discover patterns and build predictive models from a large amount of medical data. It can not only assist doctors in identifying high-risk groups in early screening, but also be used for accurate diagnosis and personalized treatment plan development.


Proposed System:

            The incidence and mortality rate of breast cancer is increasing year by year and has become the number one cancer among women worldwide. In the medical field, the diagnosis and treatment of breast cancer relies heavily on early detection and treatment, and the earlier the treatment, the better the clinical outcome for patients. Firstly, in the preprocessing sections, some categorical values are found, factorize is used to encode the categorical values into numerical. A combined SMOTE sampling method is used to solve the problem of sample imbalance. Then, the features of the dataset are screened using the mutual information method, and further the recursive feature elimination method based on the Logistic Regression is used to derive the best feature subset. Finally, four different machine learning models Random Forest, SVM, KNN, and Gradient Boost, are used for classification and prediction. The experimental results find that the best prediction results are obtained using the RF model, with the high accuracy. This is better than the previous research methods


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