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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence

Overview

This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.

Publication

Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.

DOIOpen Access

Key Findings

  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI

Data Source

The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.

MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years

Interactive Risk Calculator

Try the online risk calculator:https://alkat19.github.io/ED_Pred/

This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.

Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.

Repository Contents

FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator

Citation

If you use this code or the risk calculator, please cite:

@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}

Contact

For questions about the research or code: alexandros.katsiferis@sund.ku.dk

License

Please refer to the publication for data availability information.

About

Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
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Activity
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Watchers
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
Resources
Readme
Activity
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
Resources
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Activity
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Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
Resources
Readme
Activity
Stars
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Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence
Overview
This repository contains the code and resources for predicting eating disorders among adolescents using both register-based and self-reported data. The study encompasses data from over 40,000 adolescents, aiming to develop robust diagnostic and prognostic models.
Publication
Katsiferis A, Joensen A, Petersen LV, Ekstrøm CT, Olsen EM, Bhatt S, Nguyen TL, Strandberg Larsen K. Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence.npj Mental Health Research. 2025;4:65.
DOIOpen Access
Key Findings
  • Diagnostic model (identifying EDs by DNBC-11): AUC = 81.3 [95% CI: 78.0, 84.6]
  • Prognostic model (predicting EDs by DNBC-18): AUC = 76.9 [95% CI: 74.3, 79.5]
  • A simplified 10-predictor logistic regression achieved comparable performance to the full ML model
  • Top predictors: sex, emotional symptoms, body dissatisfaction, peer problems, stress, conduct problems, parental BMI, and childhood BMI
Data Source
The Danish National Birth Cohort (DNBC) following 96,822 children from before birth through young adulthood.
MetricValue
Diagnostic sample44,357 participants
Prognostic sample26,127 participants
Predictors evaluated~100
Follow-up period18 years
Interactive Risk Calculator
Try the online risk calculator:https://alkat19.github.io/ED_Pred/
This interactive tool implements a simplified logistic regression model using the top 10 predictors from our study, allowing users to estimate eating disorder risk based on factors measured around age 11.
Disclaimer: This calculator is for educational and research purposes only. It is NOT a diagnostic tool and should NOT replace professional clinical assessment. The model was developed using Danish data and may not generalize to other populations.
Repository Contents
FileDescription
Pre_Processing.RData cleaning, transformation, and preparation
Modelling_Diagnostic_Main.RPrimary diagnostic model development
Modelling_Diagnostic_Secondary.RSecondary (extended) diagnostic models
Modelling_Prognostic_Main.RPrimary prognostic model development
Modelling_Prognostic_Secondary.RSecondary (extended) prognostic models
Figures.RGeneration of visualizations and plots
Tables.RSummary tables and descriptive statistics
Risk_Calculator_App.RSource code for the interactive Shiny calculator
docs/Shinylive deployment files for the web-based calculator
Citation
If you use this code or the risk calculator, please cite:
@article{katsiferis2025eating,
title={Developing machine learning models of self-reported and register-based data to predict eating disorders in adolescence},
author={Katsiferis, Alexandros and Joensen, Andrea and Petersen, Liselotte Vogdrup and Ekstr{\o}m, Claus Thorn and Olsen, Else Marie and Bhatt, Samir and Nguyen, Tri-Long and Strandberg Larsen, Katrine},
journal={npj Mental Health Research},
volume={4},
pages={65},
year={2025},
publisher={Nature Publishing Group}
}
Contact
For questions about the research or code: alexandros.katsiferis@sund.ku.dk
License
Please refer to the publication for data availability information.
About
Prediction of eating disorders in 40,000+ adolescents using register-based and self-reported data
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages