Self-Healing Machine Learning Models: Automatic Error Detection and Correction in Prediction Systems

Self-Healing Machine Learning Models: Automatic Error Detection and Correction in Prediction Systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 February 2026 V1 Latest version Share on Self-Healing Machine Learning Models: Automatic Error Detection and Correction in Prediction Systems Author : Sanjana Sivakumar 0009-0006-0673-4963 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177153464.48279403/v1 124 views 66 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The decision-making processes in healthcare and finance and autonomous vehicle technology and recommendation systems now depend on Machine Learning (ML) models which have become essential components of these systems. The models used today experience errors because they were designed to operate with complete data but encounter challenges from noisy input and missing information and changes in data patterns and unexpected events. The existing methods of monitoring and retraining processes require intensive time and resource expenditures which prevent them from delivering immediate results. The result of this situation created selfhealing machine learning models which operate to find and fix problems which occur in their prediction systems. Self-healing models use continuous monitoring functions to detect anomalies and low-confidence predictions and changes in data distribution patterns. The system implements detection by applying error correction methods which include adaptive retraining and ensemble-based error correction and reinforcement learning-driven adjustments to decrease error effects while enhancing prediction accuracy. The models improve performance through automatic error detection but also decrease their need for human support which helps build strong and dependable AI systems. The creation of self-healing machine learning frameworks marks a crucial advancement toward developing artificial intelligence systems that can function correctly in changing environments which require high security. The research paper investigates the design framework and operational methods and existing obstacles and potential uses of self-healing machine learning systems which have the capacity to transform the current state of predictive system performance and independent operation. Supplementary Material File (sanjana3.pdf) Download 457.32 KB Information & Authors Information Version history V1 Version 1 19 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords adaptive model continuous learning error detection machine learning models self-healing systems Authors Affiliations Sanjana Sivakumar 0009-0006-0673-4963 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 124 views 66 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sanjana Sivakumar. Self-Healing Machine Learning Models: Automatic Error Detection and Correction in Prediction Systems. Authorea . 19 February 2026. DOI: https://doi.org/10.22541/au.177153464.48279403/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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