Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the...

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.

Advanced machine learning models, including Support Vector Machines (SVM), Neural Networks (NN) were employed to analyze the data. Two hybrid machine learning method named SVM- improved particle swarm optimization (IPSO) and NN-IPSO as hybrid SVR and NN combined with an IPSO is proposed for prediction of sleep quality. In the enhanced PSO, a local search position of particle is developed for better calibration of the parameters in NN and SVM applied in hybrid models. In local search of improved PSO, the best particle is applied with a random adjusting process applied for new particles.

These several ML methods showed that revealed significant associations between specific gut microbiota and sleep quality in endometriosis patients. The hybrid methods are more accurate than traditional machine learning methods-based NN and SVR that these methods exhibit a strong predictive tendency by using the local search. Exploring the underlying mechanisms through which the gut microbiome influences sleep could provide deeper insights into potential therapeutic targets.