Optimizing Aging Male Symptom Questionnaire Through Genetic Algorithms Based Machine Learning Techniques
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 ap' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Hypogonadism","Machine learning","Questionnaire design","Testosterone"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 ap' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 ap
Hypogonadism; Machine learning; Questionnaire design; Testosterone