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Principal Investigator
Richa Sharma
Arkus AI
Position Title
Mahine learning Intern
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
Oct 31, 2023
Machine Learning Applications in Colorectal Cancer Data Analysis: A Comprehensive Investigation
This research project aims to explore the applicability of machine learning algorithms in the analysis of colorectal cancer datasets. Colorectal cancer is a prevalent and deadly disease, and the effective application of machine learning techniques can potentially revolutionize the diagnosis, prognosis, and treatment of this condition. The project involves the acquisition of a comprehensive colorectal cancer dataset, including patient information, disease characteristics, and treatment outcomes. Using a variety of machine learning algorithms and data analysis methodologies, we will investigate the potential for accurately predicting disease progression, patient outcomes, and the influence of treatment options. The results of this study have the potential to enhance the understanding of colorectal cancer and may lead to more personalized and effective treatment strategies, ultimately improving patient care and outcomes in the realm of colorectal cancer.

Aim 1: Data Collection and Preparation
Collect a comprehensive colorectal cancer dataset, including patient demographics, disease stage, treatment details, and outcomes.
Clean, preprocess, and curate the dataset to ensure data quality and consistency.

Aim 2: Feature Selection and Engineering
Identify relevant features and variables for predicting disease progression, patient survival, and treatment effectiveness.
Create new informative features through feature engineering techniques.

Aim 3: Model Development and Selection
Explore a range of machine learning algorithms, including classification and regression models, to predict disease progression and patient outcomes.
Evaluate and compare the performance of these models to identify the most suitable for colorectal cancer analysis.