Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Optimizing Aging Male Symptom Questionnaire Through Genetic Algorithms Based Machine Learning Techniques

Authors
Kim, Jin WookMoon, Du Geon
Issue Date
1월-2021
Publisher
PUSAN NATL UNIV MEDICAL SCH, DEPT UROLOGY
Keywords
Hypogonadism; Machine learning; Questionnaire design; Testosterone
Citation
WORLD JOURNAL OF MENS HEALTH, v.39, no.1, pp.139 - 146
Indexed
SCIE
SCOPUS
KCI
Journal Title
WORLD JOURNAL OF MENS HEALTH
Volume
39
Number
1
Start Page
139
End Page
146
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/50273
DOI
10.5534/wjmh.190077
ISSN
2287-4208
Abstract
Purpose: Genetic algorithm (GA) is a machine learning optimization strategy where sample strategies compete for fitness to evolve an optimum solution. This study evolves the Aging Male Symptoms (AMS) with GA to better identify late onset hypogonadism (LOH) with serum testosterone. Materials and Methods: GA was trained on a training set of standard AMS questionnaire on a nationwide LOH epidemiology study. Random matrices of selectors for particular items were generated. Each generation of was evolved through a fitness function determined by sensitivity. Threshold to determine positive serum testosterone level for LOH was randomized for each competing strategy. After 2,000 runs, with each run producing the best result out of a set of 3,000 randomly generated sets evolved through 300 generations, the best AMS selection matrix was then applied to a separately enrolled validation set to compare outcomes. Results: Predictability for serum testosterone levels dropped markedly above 3.5 ng/mL during pilot training. Limiting the training to testosterone thresholds between 2.5 and 3.5 ng/mL the GA 93 different strategies. Only a selection of 5 items, determining for a threshold of 20 points and determining for a serum testosterone level of 3.16 ng/mL, showed robust reproducibility within the internal validation set. Applying these conditions to the independent validation set showed sensitivity improved from 0.66 to 0.77, with a specificity of 0.07 to 0.19, respectively. Conclusions: GA method of selecting questionnaires improved AMS questionnaire significantly. This method can be easily applied to other questionnaires that do not correlate with physiological markers.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Medicine > Department of Medical Science > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Moon, Du Geon photo

Moon, Du Geon
의과대학 (의학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE