Development of a ML algorithm to predict ovarian cancer risk
We want to study how several personal variables (e.g. medical history, family history, lifestyle, etc.) could interact with each other and build a Machine-Learning model around those variables to see whether such a model could improve the prediction of ovarian cancers. Lifestyle and diet components could particularly play a major role in the algorithm's predictive power as recent studies suggest (Liu et al, 2023, Chen et al, 2021).
- Check whether personal variables such as medical history, family history, lifestyle, and diet. can have some predictive power for ovarian cancer when taken altogether;
- If yes, test whether a predictive score of ovarian cancer risk could be calculated;
- If yes, the project should answer whether such a score could potentially improve ovarian cancer screening.
Nicolas Martelin, Ph.D. - Prostperia SAS
Benjamin Chen - Prostperia SAS