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
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