Breast Cancer Prediction Using Various Machine Learning Algorithms and Their Comparative Analysis

Breast cancer is a grave health risk and impacts millions of women globally. For effective treatment, it must be detected as early as possible. Use of ML-based algorithms for the breast cancer prediction is becoming widespread. Machine learning algorithms are well-suited for such applications as they can analyze vast amounts of data and have the ability to spot intricate patterns in it. In this paper, breast cancer prediction is done using various ML algorithms including Support Vector Machine (Linear, Polynomial and Radial), Boosted tree, Logistic Regression and Random Forest. Breast Cancer Wisconsin (Diagnostic) dataset has been selected for training and testing purpose. The prediction models have been compared using various performance parameters observed. Results indicate that these prediction models are effective in breast cancer prediction.