Comparative Model Analysis for Early Detection of Breast Cancer Using Clinical Data

Breast cancer detection is one of the prominent topics in medical research, as timely detection of it may help in obtaining a better treatment outcome and thus a better survival. This paper presents a comparative study of four machine learning algorithms, including Logistic Regression, Decision Trees, K-Nearest Neighbor (KNN), and the Naive Bayes algorithm, for the effective classification of breast cancer. The original data was effectively processed and missing values invalidated, and important features were normalized to obtain a well-trained model. Each of these algorithms is evaluated by means of various metrics of evaluation, including precision, recall, and F measure, which helps to understand the predictive capability of the model for each class of the model. Our experimental analysis shows that Logistic regression gives a superior class performance and also class stability, making it an appropriate algorithm for application in medical diagnosis. The results also affirm the usefulness of machine learning in practical life so that the peculiarities of illness do not permit diagnostic errors, and hospitalizations may be minimized by early intervention. This research leads up to the great mass of information put into form on methods of treatment of cancer detection through data analysis, and a field of development is opened out for future work in hybrid integration and methods of feature selection with better methods of predicting.