Classification: A Machine Learning Pipeline for Brain Tumor Analysis

The classification analysis of the brain tumor dataset was designed to predict critical clinical outcomes, particularly the likelihood of a patient receiving radiation treatment, based on a variety of patient characteristics and tumor attributes. The dataset encompassed features such as age, gender, tumor type and size, tumor stage, growth rate, surgical intervention, chemotherapy, MRI results, and family history.

A robust machine learning pipeline was constructed, incorporating tailored preprocessing for both numerical and categorical features—including imputation, scaling, encoding, and feature engineering (interaction terms, polynomial expansion, and binning). Multiple classification algorithms were evaluated, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Classifier (SVC), and K-Nearest Neighbors.

Model performance was assessed using standard classification metrics: Accuracy, Precision, Recall, F1 Score, and ROC AUC. While baseline models showed modest predictive performance, hyperparameter tuning and pipeline optimization slightly improved generalization. Gradient Boosting delivered the most consistent results post-tuning, with modest gains in ROC AUC and F1 scores.

Overall, this classification analysis provides a foundational approach to identifying treatment likelihood patterns and understanding the factors influencing clinical decision-making in brain tumor care. These insights can inform targeted intervention strategies and guide future predictive modeling efforts in oncology-focused datasets