Sensor Infused Quantum CNN for Diabetes Disease Prediction and Diet Recommendation

Sensor Infused Quantum CNN for Diabetes Disease Prediction and Diet Recommendation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sensor Infused Quantum CNN for Diabetes Disease Prediction and Diet Recommendation Jameer Kotwal, Pravin Futane, Gurunath Chavan, Archana Chaudhari, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5750023/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Diabetes management requires evidence-based recommendations that enable people to manage their health. A rising diabetes rate can lead to significant health risks and financial hardships. An early diagnosis and efficient treatment are essential to reduce the effects of diabetes. With dietary recommendations and other essential components of diabetes care, complications from diabetes may be reduced, and health can be enhanced. In this paper, a novel Sensor infused QUantum CNN for diabetes Identification and Diet recommendation (SQUID) technique has been proposed, which identifies diabetes using an IoT system and provides diet recommendations for reducing diabetes. The proposed SQUID system collects data from remote patients using IoT sensors and uses the Namib Beetle Optimization (NBO) technique to select the features. The prediction phase uses the Quantum CNN technique for classifying the input into diabetes and non-diabetes. After prediction, the suggestion phase will provide the diet recommendation using the fuzzy rule for the person affected with diabetes through the mobile application. The efficacy of the proposed SQUID framework has been assessed using specific parameters such as Accuracy (AC), Precision (PN), F1 score (F1_S), Recall (RL) and Diagnostic Odds Ratio (DOR). The SQUID framework achieves a higher AC of 98.69%, whereas HCBDA, IWBSOA and e-diagnosis achieve the AC of 92%, 94%, and 96.5%. The proposed SQUID achieved an 89.90% diagnostic odds ratio where the existing HCBDA reached 69.58%, Health-Edge reached 53.86% and IWBSOA obtained a 72.93% diagnostic odds ratio respectively. Diabetes prediction Namib beetle optimization Quantum Convolutional Neural Network Diet recommendation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Feb, 2025 Reviews received at journal 07 Feb, 2025 Reviewers agreed at journal 05 Feb, 2025 Reviews received at journal 25 Jan, 2025 Reviews received at journal 20 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers agreed at journal 10 Jan, 2025 Reviewers agreed at journal 10 Jan, 2025 Reviewers invited by journal 10 Jan, 2025 Editor assigned by journal 07 Jan, 2025 Submission checks completed at journal 07 Jan, 2025 First submitted to journal 02 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5750023","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399124279,"identity":"80b50c57-de5b-4d76-8b08-378e47de6f18","order_by":0,"name":"Jameer Kotwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACxgYwZSPHxg5iFTAwSBCpJc2Yn+cAA8MBAyK0QMHhxJkzEojUwtx+OvHBxz1piRtuPj4m/cHARk6ygfnhoxv4HNaTu9lwxjMb4w2309IkDhikGUszsBkb5+D1S+42aZ4DabIbbueYAbUcTpzHwMMmjVdL/9vtv/8cOMy44eYZYrXMyN3GzHDgsOLMGTwQLbMJa3m7WbLnACiQ05ItzgD9ItlMwC+G/bkbP/w4AIrKwwdvVFTYyEkcb374GK+WBgwhZjzKQUCegPwoGAWjYBSMAgYGAMfwUC6Bves0AAAAAElFTkSuQmCC","orcid":"","institution":"Dr. D. 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