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Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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

Jin Wook Kim¹, Du Geon Moon²

¹Department of Urology, Chung-Ang University College of Medicine, Seoul, Korea. jinwook@cau.ac.kr.

The World Journal of Men'S Health

|February 4, 2020

Summary

This summary is machine-generated.

Genetic algorithm (GA) optimization improved the Aging Male Symptoms (AMS) questionnaire for identifying late onset hypogonadism (LOH). This machine learning approach enhances diagnostic accuracy by selecting key questionnaire items correlated with serum testosterone levels.

Keywords:

Hypogonadism Machine learning Questionnaire design Testosterone

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Area of Science:

Biomedical informatics
Machine learning applications in healthcare
Endocrinology

Background:

Late onset hypogonadism (LOH) is a clinical condition associated with aging in men.
The Aging Male Symptoms (AMS) questionnaire is commonly used but may lack precision in identifying LOH.
Serum testosterone levels are a key physiological marker for diagnosing LOH.

Purpose of the Study:

To evolve the AMS questionnaire using a genetic algorithm (GA) for improved identification of LOH.
To optimize the selection of AMS questionnaire items based on serum testosterone levels.
To enhance the diagnostic accuracy of the AMS questionnaire for LOH.

Main Methods:

A genetic algorithm (GA) was trained on a nationwide LOH epidemiology study dataset.
The GA evolved strategies by generating random matrices of questionnaire item selectors.
Fitness was determined by sensitivity, with randomized thresholds for serum testosterone levels.

Main Results:

The GA identified an optimal AMS questionnaire subset of 5 items.
This optimized subset determined a threshold of 20 points and a serum testosterone level of 3.16 ng/mL.
Application to an independent validation set improved sensitivity from 0.66 to 0.77, with specificity increasing from 0.07 to 0.19.

Conclusions:

The GA-optimized AMS questionnaire significantly improved diagnostic performance for LOH.
This machine learning approach offers a robust method for refining questionnaires linked to physiological markers.
The GA methodology is adaptable for enhancing other clinical questionnaires.