This project involved developing and evaluating machine learning models to classify bone tumor patient status into three categories:
- No Evidence of Disease (NED)
- Alive with Disease (AWD)
- Deceased (D)
The models were trained and tested on a dataset from the Memorial Sloan Kettering Cancer Center with 500 samples and 9 features.
The following machine learning classification algorithms were implemented and evaluated:
- Gaussian Naive Bayes
- Decision Tree
- Random Forest
- Logistic Regression
- Linear SVC
- K-Nearest Neighbors (Euclidean and Manhattan distances)
The models were thoroughly evaluated on classification performance metrics:
- Accuracy
- Precision
- Recall
- F1 Score
- Execution Time
Among all the models, Logistic Regression achieved the highest accuracy of 81% in classifying bone tumor patient status.
Linear SVC also performed well in minimizing false negatives. In contrast, Gaussian Naive Bayes and KNN models showed limitations in distinguishing between patient statuses.
This repository contains:
- Jupyter notebooks for data preprocessing, EDA, and model implementation
- Model evaluation results
The dataset is from Kaggle: https://www.kaggle.com/datasets/antimoni/bone-tumor