earticle

영어

The rising prevalence of breast cancer across the globe requires the application of advanced diagnostic techniques for early detection and treatment. This research uses the Wisconsin Breast Cancer Dataset to explore the efficacy of various machine-learning algorithms and ensemble techniques in predicting breast cancer. The study encompasses three significant steps: data retrieval from Kaggle, data preparation through exploratory data analysis, and predictive model formulation and evaluation. Various machine learning algorithms, including Support Vector Machine (SVM) and Random Forest (RF) were employed alongside ensemble techniques like Bagging, Boosting, and a Voting Classifier that integrates multiple models. Feature selection emerged as a pivotal task, enhancing model performance by focusing on significant attributes, thus addressing challenges like high dimensionality and overfitting while promoting model interpretability. The Voting Classifier exhibited the highest accuracy of 98.25%, with varying performance across different feature sets. The insights garnered from feature selection and machine learning models demonstrate promising capabilities for early breast cancer diagnosis, emphasizing the critical role of machine learning in advancing medical data analytics for better healthcare outcomes. This research not only underscores the potential of machine learning in medical diagnostics but also provides a comprehensive exploration of feature selection and ensemble learning in achieving superior predictive accuracy in breast cancer detection.