GitHub - abeyankargiridharan/-Predicting-Mortality-and-Relapse-Free-Survival-in-Head-and-Neck-Cancer-Using-Clinical-Data: This study applies machine learning to predict mortality and relapse-free survival in head and neck squamous cell carcinoma (HNSCC) using Cancer Imaging Archive data. Models like XGBoost and SVM achieved over 90% accuracy. Key predictors include smoking, treatment type, and loco-regional control, aiding personalized treatment strategies.

abeyankargiridharan / -Predicting-Mortality-and-Relapse-Free-Survival-in-Head-and-Neck-Cancer-Using-Clinical-Data Public

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This study applies machine learning to predict mortality and relapse-free survival in head and neck squamous cell carcinoma (HNSCC) using Cancer Imaging Archive data. Models like XGBoost and SVM achieved over 90% accuracy. Key predictors include smoking, treatment type, and loco-regional control, aiding personalized treatment strategies.

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Name		Name	Last commit message	Last commit date
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Code_2_HNSCC.ipynb		Code_2_HNSCC.ipynb
Precision_Oncology_HNSCC.ipynb		Precision_Oncology_HNSCC.ipynb
Precision_Oncology_VitalStatus_RFS_HNSCC.pdf		Precision_Oncology_VitalStatus_RFS_HNSCC.pdf