- Linear prognostic models:
- I built a linear prognostic model using logistic regression,
- then evaluate the model by calculating the concordance index
- I finally improved the model by adding feature interactions.
- Prognosis with Tree-based models
- I tuned decision tree and random forest models to predict the risk of a disease,
- then evaluated the model performance using the c-index.
- In order to improve model performance, I identified missing data and how it may alter the data distribution,
- then use imputation to fill in missing data.
- Survival Models and Time
- Work with data where the time that a disease occurs is a variable.
- I used this data to build more flexible models that can predict the 5 year, 7 year, or 10 year risk.