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TEAM

Cancer Survivability

Dilruba Sofia, Funmilola Mary Taiwo, Enayon Sunday Taiwo, Samuel Ogunfuye, Karla Paulette Flores Silva, Ray Lee

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Unfortunately, each of us has a 1/4 chance of getting cancer. Although with advances in treatment technologies, the survival rate of cancer patients has increased, cancer still kills many people.
The idea for this project is to predict cancer patient survival duration based on their tumor biopsy slides and other clinical features such as tumor location, type, stage, immune score, etc.
Data source: The Cancer Genome Atlas (TCGA portal)
Method: For data preparations, we can probably extract image information through PCA, get immune scores from the patients' immune cell abundance, and combine this information with other clinical features. Then we can apply one or a couple of classification algorithms such as KNN or Random Forest to predict patients' outcomes.

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