Ovarian Cancer Classification
Principal Investigator
Name
Lakshmi kumari
Degrees
Ph.D
Institution
Puducherry Technological University
Position Title
Research scholar
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1443
Initial CDAS Request Approval
Jan 16, 2024
Title
Ovarian Cancer Classification
Summary
In this direction, a methodology is designed to classify between Benign Ovarian and Tumor.
To identify an efficient machine learning models on a comprehensive dataset including blood samples, general chemistry medical tests, and bio markers.
To identify an efficient machine learning models on a comprehensive dataset including blood samples, general chemistry medical tests, and bio markers.
Aims
We have to develop a machine learning model using clinical data for the early diagnosis of ovarian cancer. Our model ensemble consists of various machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), and Convolutional Neural Network (CNN). These models were trained to classify patients into benign and malignant ovarian cancer categories.
Collaborators
Dr P.Maragathavalli,
Professor
Information Technology
Puducherry Technological University