BONE CANCER DETECTION

Bone tumors pose a significant health concern due to their potential malignancy and impact on the skeletal system. Early and accurate detection is critical for effective treatment and prognosis. This project leverages machine learning techniques to classify bone tumors using a publicly available dataset from Kaggle. The dataset includes various features relevant to tumor characteristics, which are analyzed and used to build predictive models. Four powerful classification algorithms Decision Tree, Random Forest, CatBoost, and XGBoost are employed to evaluate and compare performance in terms of accuracy, precision, recall, and F1-score. These models are trained and tested to differentiate between benign and malignant tumors, facilitating automated diagnostic support. The study aims to identify the most effective model for bone tumor classification, contributing to the development of intelligent diagnostic tools in the medical domain. The results highlight the potential of ensemble learning techniques in improving classification accuracy and supporting clinical decision-making. Keywords: Bone Tumor Classification, Machine Learning, Decision Tree, Random Forest, CatBoost, XGBoost, Medical Diagnosis, Kaggle Dataset, Ensemble Learning, Tumor Detection.