Innovative approaches towards early stage ovarian cancer prediction using Artificial Intelligence

Principal Investigator

Name
Subbulakshmi P

Degrees
ME. PhD

Institution
VELLORE INSTITUTE OF TECHNOLOGY

Position Title
ASSISTANT PROFESSOR

Email
subbulakshmi.p@vit.ac.in

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCOI-1591

Initial CDAS Request Approval
Jun 17, 2024

Title
Innovative approaches towards early stage ovarian cancer prediction using Artificial Intelligence

Summary
I am conducting a study on the early detection of ovarian cancer using Machine Learning (ML) and eXplainable Artificial Intelligence (XAI) techniques. To evaluate various predictive models, including Support Vector Machines, Decision Trees, and ensemble methods, I require access to a dataset with clinical features such as age, family history, tumor markers, and imaging characteristics. To enhancing our predictive models and improving early diagnosis strategies we kindly request you to provide the PLCO dataset.

Aims

-> Assess the performance of multiple ML algorithms
-> Integrate eXplainable Artificial Intelligence (XAI) methodologies to provide transparent and interpretable predictions, making the decision-making process of complex ML models understandable for healthcare professionals and patients.
-> Apply preprocessing techniques to improve the quality of the input data and effective feature selection to enhance model performance
-> Offer a comprehensive approach for integrating ML and XAI in the early detection of ovarian cancer, contributing to improved clinical practices and patient outcomes in the fields of oncology and women’s health

Collaborators

Subbulakshmi P , Sheela Lavanya J M