Abstract:
Breast cancer is a critical health issue affecting women worldwide, making early and accurate diagnosis crucial for effective treatment and improved patient outcomes. Machine learning techniques, such as logistic regression, have shown promise in assisting with breast cancer classification based on various clinical and pathological features.
This abstract presents a comprehensive study on the classification of breast cancer using logistic regression. The primary objective is to explore the effectiveness of logistic regression as a predictive modeling technique for distinguishing between malignant and benign breast tumors. The study utilizes a dataset consisting of clinical and histopathological attributes of breast cancer cases, including patient age, tumor size, lymph node status, and tumor grade.
The logistic regression model is trained and evaluated using the dataset, employing appropriate feature selection, data preprocessing, and model validation techniques. The performance of the logistic regression model is assessed using metrics such as accuracy, precision, recall, and F1-score. The results are compared against other commonly used machine learning algorithms to gauge the effectiveness of logistic regression in breast cancer classification.
The findings of this study demonstrate the potential of logistic regression as a reliable classifier for breast cancer diagnosis. The logistic regression model achieves a high accuracy rate and exhibits robustness in distinguishing between malignant and benign breast tumors. The selected features contribute significantly to the model’s predictive performance, emphasizing the importance of appropriate feature selection in achieving accurate classification results.
The implications of this study are significant for clinical decision-making, as it provides valuable insights into the potential use of logistic regression as a tool for assisting healthcare professionals in breast cancer diagnosis. Logistic regression offers a transparent and interpretable framework, enabling medical practitioners to understand the underlying factors contributing to breast cancer classification decisions.
In conclusion, this study highlights the effectiveness of logistic regression in classifying breast cancer cases. The findings contribute to the growing body of research in the field of machine learning-assisted medical diagnostics and pave the way for the development of more accurate and efficient breast cancer classification systems.
Keywords: Breast cancer, Logistic regression, Machine learning, Classification, Diagnosis.
CLASSIFICATION OF BREAST CANCER USING LOGISTIC REGRESSION. GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
