Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.
Journal:
Computer methods and programs in biomedicine
Published Date:
Apr 26, 2025
Abstract
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of the gut microbiome in regulating various physiological processes, including sleep. This study investigates the relationship between gut microbiome composition and sleep quality in patients with endometriosis using machine learning (ML) techniques named artificial neural network (ANN) and support vector regression (SVR) with several hybrid approaches as ML-based ANN and SVR coupled with optimization using partial swarm optimization (PSO) and an improved PSO. We analyzed data from 200 endometriosis patients, encompassing a diverse range of age, Body mass index (BMI), symptom severity, and lifestyle factors. Key gut microbiota, including Bacteroides, Prevotella, Ruminococcus, Lactobacillus, Faecalibacterium, and Akkermansia, were quantified. Additionally, lifestyle variables such as diet quality, physical activity level, daily caloric intake, fiber intake, sugar intake, alcohol consumption, smothking status are applied for predictions of sleep quality.
