Logistic regression has many applications in data science, but in the world of healthcare, it can really drive life-changing action. Logistic regression, a statistical analysis method, is utilized in data science as a Machine Learning algorithm for classification and predictive analysis. In this context, a logistic regression model is built to examine the correlation between nine independent variables (clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell, bare nuclei, bland chromatin, normal nucleoli, and mitoses) and tumor class (benign or malignant) in breast cancer.
The process involves data preprocessing, which includes importing the dataset and splitting it into training and test sets. Next, the logistic regression model is trained on the training set and used to predict results for the test set. The model's performance is evaluated using a confusion matrix and accuracy computed with k-Fold cross-validation, providing valuable insights for decision-making and identifying significant predictors of breast cancer. The success of the model hinges on the accurate identification of mammogram findings by radiologists.
Dataset: Wolberg,WIlliam. (1992). Breast Cancer Wisconsin (Original). UCI Machine Learning Repository. https://doi.org/10.24432/C5HP4Z.