[{"command":"settings","settings":{"pluralDelimiter":"\u0003","suppressDeprecationErrors":true,"user":{"uid":0,"permissionsHash":"d9587e6f410d2e7f476e3da6cb10a457c78ab82347f962bf83d9020620f901dd"}},"merge":true},{"command":"add_css","data":[{"rel":"stylesheet","media":"all","href":"\/modules\/contrib\/addtocal\/addtocal.css?t2408i"},{"rel":"stylesheet","media":"all","href":"\/themes\/custom\/cest2025\/css\/components\/node.css?t2408i"}]},{"command":"add_js","selector":"body","data":[{"src":"\/core\/assets\/vendor\/jquery\/jquery.min.js?v=3.7.1"},{"src":"\/core\/assets\/vendor\/once\/once.min.js?v=1.0.1"},{"src":"\/core\/misc\/drupalSettingsLoader.js?v=10.5.1"},{"src":"\/core\/misc\/drupal.js?v=10.5.1"},{"src":"\/core\/misc\/drupal.init.js?v=10.5.1"},{"src":"\/modules\/contrib\/addtocal\/addtocal.js?v=10.5.1"},{"src":"\/modules\/contrib\/addtocal\/addtocal-download.js?v=10.5.1"}]},{"command":"openDialog","selector":"#drupal-modal","settings":null,"data":"\n\u003Carticle class=\u0022node node--type-presentation node--promoted node--view-mode-modal\u0022\u003E\n      \u003Cdiv\u003ESession 20 - Wastewater treatment (4) \u003C\/div\u003E\n  \n      \u003Cb\u003E\u003Cspan\u003EUse of wastewater-based epidemiology data in an artificial neural network to estimate type 2 diabetes mellitus prevalence\u003C\/span\u003E\n\u003C\/b\u003E\n  \n      \u003Cdiv\u003E\u003Cb\u003ECEST ID: cest2025_00155\u003C\/b\u003E\u003C\/div\u003E\n  \n        \u003Cdiv class=\u0022mb-3\u0022\u003E\n      \u003Cb\u003ERoom Aegle B | Fri 5 Sep 2025 | 12:15 - 12:20 pm\u003C\/b\u003E\n    \u003C\/div\u003E\n  \n          \n    \n  \n      \u003Cdiv class=\u0022mt-10\u0022\u003E\n            \u003Cdiv class=\u0022clearfix text-formatted field field--name-presentation-body field--type-text-long field--label-hidden field__item\u0022\u003EDiabetes mellitus (DM) accounts for 90% of global diabetes cases and is commonly managed using metformin which is detectable in wastewater. Wastewater-based epidemiology (WBE) is the measurement of this biomarker\u2019s concentration and consumption from wastewater samples to estimate DM prevalence rates. Artificial neural networks (ANNs) can be utilized to handle complex nonlinear datasets, but limited research exists on using ANNs with WBE for DM monitoring. This study aims to enhance the accuracy of DM prevalence rate estimations using ANNs with WBE, demographic, socioeconomic, and healthcare parameters. Using python, statistical analysis, specifically histograms, box plots, scatter plots and multiple linear regression analysis (MLRA) (R2=0.548), revealed skewed data sets and weak linear correlations, stressing the need for ANN modeling. The provinces of China were randomly split into 21 for training and 10 for testing. In total, 126 models were made \nwith varying combinations of architectures, variables, and inputs guided by SHAP sensitivity analysis. Based on the lowest mean squared error (MSE) from the actual DM prevalence, 12 were selected for optimization with risk factors as additional features. The best model had a MSE of 6.7365 which is lower than 12.8791 obtained from the correlation equation of Zhou et al (2024). Deviating from the control ANN, it only has WBE data and treatment rate as its features with epoch set at 500, which suggests more features create noise and higher epoch causes overfitting. This study demonstrates the huge potential of WBE integrated with ANNs for accurate disease monitoring.\u003C\/div\u003E\n      \u003C\/div\u003E\n  \n  \u003Cdiv class=\u0022mt-5 mb-5\u0022\u003E\n          \u003Cspan\u003E\n          \u003Cb\u003EPresenters:\u003C\/b\u003E\n                      \u003Cp\u003E\n            Nikolas Adonar Vallesteros\n            \u003C\/p\u003E\n                      \u003Cp\u003E\n            Gwyneth Ross Bukuhan\n            \u003C\/p\u003E\n                      \u003Cp\u003E\n            Jasmine Lois Caballes\n            \u003C\/p\u003E\n                      \u003Cp\u003E\n            \u003Ca href=\u0022\/person\/prof-florencio-ballesteros\u0022 hreflang=\u0022en\u0022\u003EProf. Florencio Ballesteros\u003C\/a\u003E\n            \u003C\/p\u003E\n                  \u003C\/span\u003E\n      \u003C\/div\u003E\n\n  \u003Cdiv class=\u0022mb-5\u0022\u003E\n          \u003Cdiv class=\u0022field__label\u0022\u003E\n        Authors\n      \u003C\/div\u003E\n              \u003Cp\u003E\n          Nikolas Adonar Vallesteros\n        \u003C\/p\u003E\n              \u003Cp\u003E\n          Gwyneth Ross Bukuhan\n        \u003C\/p\u003E\n              \u003Cp\u003E\n          Jasmine Lois Caballes\n        \u003C\/p\u003E\n              \u003Cp\u003E\n          Florencio Ballesteros\n        \u003C\/p\u003E\n            \u003C\/div\u003E\n\n\u003C\/article\u003E\n","dialogOptions":{"width":"700","position":{"my":"right top","at":"right top"},"closeOnEscape":true,"dialogClass":"presentation-dialog","modal":true,"title":"","classes":{"ui-dialog":"presentation-dialog"}}}]