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All Subjects: Machine Learning
Member of: Barrett, The Honors College Thesis/Creative Project Collection
Resource Type: Text
Status: Published

Topological Descriptors for Parkinson's Disease Classification and Regression Analysis

Description

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.

ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)

Created2020-05

Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques

Description

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.

ContributorsLattus, Robert (Author) / Dasarathy, Gautam (Thesis director) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)

Created2021-12

Augnosis: Self-Diagnosis in Augmented Reality

Description

Oftentimes, patients struggle to accurately describe their symptoms to medical professionals, which produces erroneous diagnoses, delaying and preventing treatment. My app, Augnosis, will streamline constructive communication between patient and doctor, and allow for more accurate diagnoses. The goal of this project was to create an app capable of gathering data on visual symptoms of facial acne and categorizing it to differentiate between diagnoses using image recognition and identification. “Augnosis”, is a combination of the words “Augmented Reality” and “Self-Diagnosis”, the former being the medium in which it is immersed and the latter detailing its functionality.

ContributorsGoyal, Nandika (Author) / Johnson, Mina (Thesis director) / Bryan, Chris (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)

Created2022-05