Lung Cancer Risk Analysis
Principal Investigator
Name
Danbir Rashid
Degrees
B.Sc in Computer Science & Engineering
Institution
Leading University
Position Title
Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1187
Initial CDAS Request Approval
Jan 16, 2024
Title
Lung Cancer Risk Analysis
Summary
I would like to have the data for my thesis titled “Lung Cancer Risk Analysis’’ based on various Machine Learning models. My plan is to use at least 10 ML models(i.e.- Logistic Regression, Decision Tree, Random Forest, KNN, Naive Bayes, Support Vector Machine, Gradient Boosting, XGBoost, AdaBoost, ANN, etc.) to classify the cancer and for fine tuning I decide to use ensemble voting techniques. Then, find outs which features are mostly reasonable to the consequences of cancer cell. And I also planned to convert my pre-trained model into a more deployable model that are reliable to current research on Lung Cancer by using various Deep Learning techniques for example Transfer Learning to analysis the stage of the cancer cell, etc.
Aims
1. Aim to find the most impactful factors for Lung Cancer.
2. Aim to deploy my research on web and app applications that can also help other patients or a normal person to be alert on the risk factors.
3. Improve my skills on Machine Learning and Deep Learning.
Collaborators
Md. Jehadul Islam Mony, Lecturer, Department of CSE, Leading University