Lung Cancer Risk Analysis

Principal Investigator

Name
Danbir Rashid

Degrees
B.Sc in Computer Science & Engineering

Institution
Leading University

Position Title
Student

Email
drd888335@gmail.com

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