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AN ANALYTICAL FRAMEWORK TO ASSESS LIPOSUCTION OUTCOMES.
| Title: | AN ANALYTICAL FRAMEWORK TO ASSESS LIPOSUCTION OUTCOMES. |
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| Name(s): |
Patel, Kaivan , author Pandya, Abhijit , Thesis advisor Florida Atlantic University, Degree grantor Department of Computer and Electrical Engineering and Computer Science College of Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2024 | |
| Date Issued: | 2024 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 93 p. | |
| Language(s): | English | |
| Abstract/Description: | Liposuction is a common invasive procedure. Liposuction is performed for cosmetic and non-cosmetic reasons. Its use in regenerative medicine has been increasing. Its invasive nature renders it to have complications which can cause limitations in patients' recovery and patient lives. This thesis’s aim is to create an analytical framework to assess the liposuction procedure and its outcomes. The fundamental requirement to create this framework is to have a complete understanding of the procedure which includes preparation and planning of the procedure, correctly performing the procedure and ensuring patient safety on day 0, week 2, week 4, and week 12 of the procedure. 54 patient’s liposuction outcomes were followed till week 12. Data collection is the first part of the framework, which involves understanding the complex surgical outcomes. Algorithms that have been previously studied to assess morbidities and mortalities have been used in this framework to assess if they can assess liposuction outcomes. In this framework algorithms like decision tree, XG boost, random forest, support vector classifier, k nearest neighbor, k means, k fold validation have been used. XG boost performed best to assess liposuction outcomes without validation. However, after cross validation other algorithms which are random forest, support vector machine and KNN classifier outperformed XG boost. This framework allows to assess liposuction outcomes based on the performance of the algorithms. In future, researchers can use this framework to assess liposuction as well as other surgical outcome. | |
| Identifier: | FA00014429 (IID) | |
| Degree granted: | Thesis (MS)--Florida Atlantic University, 2024. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Liposuction Outcome assessment (Medical care) Surgery Algorithms |
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| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014429 | |
| Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU |
