2026-09-19T08:24:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/612852022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randall Davis.
Felch, Kristen (Kristen M.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-02-23T14:41:47Z
2011-02-23T14:41:47Z
2010
http://hdl.handle.net/1721.1/61285
702639074
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
"February 2010." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 43).
The Clock Drawing Test (CDT) is a medical test for neurodegenerative diseases that has been proven to have high diagnostic value due to its ease of administration and accurate results. In order to standardize the administration process and utilize the most current machine learning tools for analysis of CDT results, the digitizing pen has been used to computerize this diagnostic test. In order to successfully integrate digitizing pen technology with the CDT, a digit recognition algorithm was developed to reduce the need for manual classification of the data collected and maintain the ease of administration of the test. In addition, the Multitool Data Analysis Package was developed to aid in the exploratory data analysis stage of the CDT. This package combines several existing machine learning tools with two new algorithm implementations to provide an easy-to-use platform for discovering trends it CDT data.
by Kristen Felch.
M.Eng.
iv, 69 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Integrating digitizing pen technology and machine learning with the Clock Drawing Test
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="52488ae1-8ac5-4477-a4fe-a95ef3ef0b2a">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Integrating digitizing pen technology and machine learning with the Clock Drawing Test</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2010</PublicationDate>
<Authors>
<Author>
<DisplayName>Felch, Kristen (Kristen M.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>The Clock Drawing Test (CDT) is a medical test for neurodegenerative diseases that has been proven to have high diagnostic value due to its ease of administration and accurate results. In order to standardize the administration process and utilize the most current machine learning tools for analysis of CDT results, the digitizing pen has been used to computerize this diagnostic test. In order to successfully integrate digitizing pen technology with the CDT, a digit recognition algorithm was developed to reduce the need for manual classification of the data collected and maintain the ease of administration of the test. In addition, the Multitool Data Analysis Package was developed to aid in the exploratory data analysis stage of the CDT. This package combines several existing machine learning tools with two new algorithm implementations to provide an easy-to-use platform for discovering trends it CDT data.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>