REK-SURV: A High-Efficiency Deep Survival Analysis Model Based on Kolmogorov-Arnold Networks with a Residual Mechanism

REK-SURV: A High-Efficiency Deep Survival Analysis Model Based on Kolmogorov-Arnold Networks with a Residual Mechanism | 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 Article REK-SURV: A High-Efficiency Deep Survival Analysis Model Based on Kolmogorov-Arnold Networks with a Residual Mechanism Jinggui Xiao, Shan Hu, Xiaoling Deng, Dai Fen, Junchao You, Guangnan Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6171488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agricultural disease prediction and early warning systems are essential for safeguarding crop health, optimizing yield, and ensuring global food security. These systems rely on robust statistical methods for accurate disease outbreak forecasting. Survival analysis, a widely used approach in medical research, presents a promising solution due to its ability to model time-dependent events and predict critical occurrences, such as the onset of diseases in crops. In deep survival analysis, Multilayer Perceptrons (MLPs) are widely used models; however, they face significant limitations when applied to high-dimensional data, including overfitting, lack of interpretability, low parameter efficiency, and processing bottlenecks. To address these challenges, we introduce the Kolmogorov-Arnold Network (KAN) in the context of deep survival analysis. KAN introduces an innovative network architecture that effectively mitigates the aforementioned issues, while demonstrating exceptional function approximation capabilities and enhanced parameter efficiency. We propose REK-SURV, a deep survival model based on the KAN architecture, which incorporates a residual mechanism and an enhanced loss function. We evaluate the model using five real-world medical datasets, and the results demonstrate that REK-SURV significantly outperforms existing models, achieving superior c-index values with considerably fewer parameters and faster inference speed. By leveraging the highly efficient 'Efficient KAN' architecture, REK-SURV not only accelerates both training and inference processes but also offers a distinct advantage for deployment in resource-constrained environments. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Predictive medicine Deep Survival Analysis Kolmogorov-Arnold Networks Residual Mechanism Agricultural disease prediction Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6171488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443247109,"identity":"197cbcaa-d7b5-40c7-b622-5dab6a02c9fc","order_by":0,"name":"Jinggui Xiao","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jinggui","middleName":"","lastName":"Xiao","suffix":""},{"id":443247110,"identity":"c0e2c35f-4c1a-400a-a9e8-3441646460f3","order_by":1,"name":"Shan Hu","email":"","orcid":"","institution":"South China Agricultural 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