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Using Machine Learning for Liver Disease Classification

In this project, we will use several techniques in machine learning and data mining in order to detect Hepatitis, fibrosis, and cirrhosis. We will use a database set of 615 instances that includes important laboratory and demographic values like age. The data are collected from blood donors and Hepatitis C patients. Full data description is available at https://archive.ics.uci.edu/ml/datasets/HCV+data.

The research includes:

  1. Developing classifiers for predicting in order to detect Hepatitis, fibrosis, and cirrhosis using decision trees, support vector machines, neural networks, Bayesian classifies, and/ or some other data mining and machine learning techniques.
  2. Evaluating their performance, and comparing their accuracy.
  3. Employing Python as a primary programming language in the development process.

Focus Areas

Computer Science

Project Duration

8 weeks (June 7-July 30)

Prerequisite Courses

CSCI 204: Data Structure and Algorithms Programming using Python

Preferred Courses

CSCI 370: Data Mining

Number of Positions

1

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