[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118595-en":3,"doc-seo-118595-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118595,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Prediction of Sleep Disorders Using Novel Decision Support Neutrosophic-Based Machine Learning Model","Sleep disorders significantly impact human health, productivity, and overall quality of life, making early diagnosis and prediction essential for effective treatment. The study presents a novel decision support system that uses a neutrosophic machine learning prediction model to improve accuracy and reliability. By incorporating neutrosophic logic with truth, indeterminacy, and falsity, the method manages uncertainty and inconsistencies in sleep-related data, integrating demographics, clinical parameters, and polysomnography details. Experimental results show improved predictive accuracy, robustness, and interpretability, supporting clinician assessment under inherent medical data uncertainty.","Prediction of sleep disorders using Novel decision support neutrosophic based machine learning models  \nNihar Ranjan Panda1, Surapati Paramanik2, Prasanta Kumar Raut3, Ruchi Bhuyan4*  \n1Department of Medical Research. IMS & SUM Hospital, SOA Deemed to be university, India.  \n[1](1Email: niharranjanpanda@soa.ac.in)[Email: niharranjanpanda@soa.ac.in](1Email: niharranjanpanda@soa.ac.in)  \n2Department of Mathematics, Nandalal Ghosh B.T. College Panpur, Narayanpur, Dist-North 24  \nParganas, W. B.-743126, India.  \n2Email: [surapati.math@gmail.com](surapati.math@gmail.com)  \n3Department of Mathematics, Trident Academy of Technology, Bhubaneswar, Odisha, India.  \n[3](3Email: prasantaraut95@gmail.com)[Email: prasantaraut95@gmail.com](3Email: prasantaraut95@gmail.com)  \n4Institute of Dental science, SOA Deemed to be university, India  \n4Email: [ruchibhuyan@soa.ac.in](ruchibhuyan@soa.ac.in)  \n*Correspondence: [ruchibhuyan@soa.ac.in](ruchibhuyan@soa.ac.in)  \nAbstract: Sleep disorders significantly impact human health, productivity, and overall quality of life. Early diagnosis and prediction of these disorders are crucial for effective treatment. This study introduces a novel decision support system utilizing a neutrosophic machine learning prediction model to enhance the accuracy and reliability of sleep disorder diagnosis. Unlike traditional machine learning approaches, our model integrates neutrosophic logic, which considers three values—truth, indeterminacy, and falsity—to effectively handle uncertainty and inconsistencies in sleep-related data. The proposed model processes diverse patient information, including demographics, clinical parameters, and polysomnography details, ensuring comprehensive analysis. Experimental results demonstrate that our approach surpasses conventional machine learning methods in predictive accuracy, robustness, and interpretability. Furthermore, this research provides an advanced framework  \nfor clinicians to assess potential sleep disorders while accommodating inherent uncertainties in medical data. The study highlights the impact of neutrosophic machine learning in healthcare decision support systems and outlines potential avenues for future research.  \nKeywords: Neutrosophic sets, Machine Learning, Uncertainty handling, Sleep disorder, Classification  \n1. Introduction  \nSleep disorders represent a significant public health issue impacting large sections of the global population and simultaneously posing immense risks for respiratory, cardiovascular, metabolic, psychiatric, cognitive, and neurologic disorders [1,2] . Yet, traditional diagnostic modalities for sleep disorders are heavily reliant on subjective self-report measures and polysomnography, a resource intensive, costly, and time-consuming diagnostic test. Over the last couple of years, machine learning has been recognized as a game changer in medical diagnostics technology due to its ability to rapidly and accurately predict disease states after analyzing complex data [3, 4] . Traditional ML models may not have a clear promise in working against the uncertain or ambiguous information that is a natural part of the medical lexicon, but uncertainty is even higher after a sleep study is carried out.  \nNeutrosophy is a branch of mathematics that aims to add to and improve classical and fuzzy set theories. It has become an important tool for dealing with vagueness, indeterminacy, and uncertainty in many areas. In this situation, neutrosophic sets and their more complex forms, such as Fermatean neutrosophic sets, have gotten a lot of attention because they are good at showing uncertainty and making decisions easier. Neutrosophic sets have the basic parts of truth, uncertainty, and falsity, which makes them a better way to show complicated events. Fermateanneutrosophic sets, as an extension, provide more flexibility and accuracy, thereby expanding their use in complex decision-making and computational challenges.  \nA multitude of research articles have b","cbCaisSKQ3rd0ydB","https://ap.wps.com/l/cbCaisSKQ3rd0ydB","pdf",1230640,1,18,"English","en",105,"# Introduction\n## Neutrosophic concepts for uncertainty modeling\n## Motivation for neutrosophic-based machine learning\n## Proposed approach for sleep disorder prediction","[{\"question\":\"Why are early prediction and diagnosis of sleep disorders important?\",\"answer\":\"Sleep disorders affect health and daily functioning, and timely prediction is crucial to enable effective treatment and reduce related risks.\"},{\"question\":\"How does the neutrosophic approach help with uncertainty in medical sleep data?\",\"answer\":\"Neutrosophic logic models truth, indeterminacy, and falsity, allowing the system to handle noisy, incomplete, or contradictory information more effectively than classical ML.\"},{\"question\":\"What types of patient information does the proposed model use?\",\"answer\":\"The model integrates demographics, clinical parameters, and polysomnography details to support comprehensive analysis and prediction.\"}]","Prediction of Sleep Disorders Using Novel Decision Support Neutrosophic-Based Machine Learning Model | 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are early prediction and diagnosis of sleep disorders important?","Question",{"text":75,"@type":76},"Sleep disorders affect health and daily functioning, and timely prediction is crucial to enable effective treatment and reduce related risks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the neutrosophic approach help with uncertainty in medical sleep data?",{"text":80,"@type":76},"Neutrosophic logic models truth, indeterminacy, and falsity, allowing the system to handle noisy, incomplete, or contradictory information more effectively than classical ML.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of patient information does the proposed model use?",{"text":84,"@type":76},"The model integrates demographics, clinical parameters, and polysomnography details to support comprehensive analysis and 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