Data Analysis on Classifying the Severity of Genetic Mutations

Data Analysis on Classifying the Severity of Genetic Mutations

    Presenter(s)

    Kelly Laureen Pleiman

    Description

    Cancer tumors can have thousands of mutations but determining which of those mutations actually contribute to tumor growth is critical in understanding the disease. Through the use of productive models in machine learning, this capstone project focuses on determining the severity of different genetic mutations using available data from Kaggle on the mutation’s gene, variation, and clinical text evidence. By performing data analysis and applying different models on this complex data set, the class or severity of genetic mutations on a scale from 1-9 can be predicted. Decision tree, random forest, SVD, logistic regression, and K nearest neighbor are among the models that were used to classify genetic variation. Obtaining higher model accuracies allows for better classification of genetic mutations and could eventually expedite the time pathologists spend manually classifying mutations.

    Publication Date

    4-22-2021

    Project Designation

    Capstone Project

    Primary Advisor

    Ying-Ju Chen

    Primary Advisor's Department

    Mathematics

    Keywords

    Stander Symposium project, College of Arts and Sciences

    United Nations Sustainable Development Goals

    Industry, Innovation, and Infrastructure

    Data Analysis on Classifying the Severity of Genetic Mutations

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