[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121345-en":3,"doc-seo-121345-105":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},121345,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Optimized Machine Learning Models For Early Detection Of Alcohol Use Disorder - A Hybrid Approach","Alcohol Use Disorder (AUD) remains a critical global health issue, requiring advanced diagnostic and predictive systems for early and accurate detection. Conventional clinical and questionnaire-based methods often suffer from subjectivity and limited predictive capability, motivating intelligent computational approaches. This research proposes a hybrid optimization framework that combines machine learning with metaheuristic optimization and deep learning to select informative features, fine-tune hyperparameters, and reduce overfitting using multi-source clinical, behavioral, and neuroimaging data. Comparative evaluations and explainable AI enhance accuracy, interpretability, and data-driven decision-making for early intervention.","Optimized Machine Learning Models For Early Detection Of Alcohol Use Disorder: A Hybrid  \nApproach  \nDr. B. Santhosh Kumar1, Dr. D. Kumaresan2*  \n1Assistant Professor, Department of Computer Applications, Periyar Arts College, Cuddalore, Tamil Nadu, India.  \n2 *Department of Computer and Information Science, Faculty of Science, Annamalai University, Annamalai Nagar, TamilNadu, India.  \n[Email:](Email:1santhoshcdm@gmail.com)[1](Email:1santhoshcdm@gmail.com)[santhoshcdm@gmail.com](Email:1santhoshcdm@gmail.com)  \nCorresponding Email:2*[aucsedks@yahoo.co.in](aucsedks@yahoo.co.in)  \n\n| KEYWORDS | ABSTRACT: |\n| --- | --- |\n| Alcohol Use | Alcohol Use Disorder (AUD) remains a critical global health issue, |\n| Disorder | necessitating the development of advanced diagnostic systems for early and |\n| (AUD), | accurate detection. Conventional diagnostic methods often exhibit |\n| Machine | subjectivity and limited predictive capability, emphasizing the need for |\n| Learning | intelligent computational techniques. This research introduces a hybrid |\n| Optimization, | optimization framework that integrates machine learning models with |\n| Metaheuristic | metaheuristic optimization strategies to enhance AUD detection. By |\n| Algorithms, | incorporating evolutionary algorithms, such as Genetic Algorithms (GA) |\n| Deep | and Particle Swarm Optimization (PSO), alongside deep learning |\n| Learning | techniques, the proposed approach optimally selects features, fine-tunes |\n| Models, | hyperparameters, and reduces overfitting. A diverse dataset combining |\n| Explainable | clinical, behavioral, and neuroimaging data is used to train and validate the |\n| AI (XAI), | model, ensuring broad applicability across different populations. |\n| And Clinical | Comparative evaluations with traditional machine learning models indicate |\n| Decision | that the hybrid-optimized method substantially improves classification |\n| Support | accuracy, sensitivity, and specificity in differentiating AUD from non-AUD |\n| Systems. | cases. Additionally, explainable AI techniques are utilized to improve model interpretability, aiding healthcare professionals in understanding key predictive factors. The results highlight the potential of hybrid optimization in machine learning for AUD diagnosis, contributing to more reliable, datadriven clinical decision-making and early intervention strategies. |\n\n1. Introduction  \nAlcohol Use Disorder (AUD) remains a major global health issue, requiring the development of advanced predictive and diagnostic tools. Traditional assessment methods, such as selfreported questionnaires and clinical interviews, are often subjective and may lack accuracy. With the rise of artificial intelligence (AI) and machine learning (ML), researchers have explored data-driven approaches to enhance the identification and classification of AUD. Furthermore, the integration of metaheuristic optimization techniques within ML frameworks has been employed to improve model efficiency, optimize feature selection, and reduce  \noverfitting. This section provides an overview of significant studies that have contributed to ML-based AUD detection, focusing on optimization methods, EEG-based classification, feature selection, and ensemble learning strategies.  \nTraditional diagnostic approaches for AUD are predominantly reliant on subjective clinical assessments and self-reported data, which are inherently vulnerable to inaccuracies and biases. This inherent subjectivity accentuates the urgent need for objective, data-driven methodologies that can facilitate earlier, more accurate detection and intervention. In recent years, Machine Learning (ML) and optimization techniques have gained prominence as powerful tools in this domain, offering the potential to analyze complex datasets and uncover patterns that might otherwise remain undetected through conventional diagnostic techniques.  \nThis study introduces a hybrid optimization framework that synergistically integrates mach","cbCaiagvG7V8xiy4","https://ap.wps.com/l/cbCaiagvG7V8xiy4","pdf",432396,1,10,"English","en",105,"# Introduction\n## Motivation and limitations of traditional AUD assessment\n## Hybrid optimization framework for ML-based AUD detection\n## Related work: optimization, EEG-based classification, and feature extraction","[{\"question\":\"Why is early and accurate detection of Alcohol Use Disorder (AUD) important?\",\"answer\":\"AUD is a major global health issue where earlier identification enables timely intervention. Early detection also helps address inaccuracies and biases common in conventional assessments.\"},{\"question\":\"What does the proposed hybrid approach combine to improve AUD detection?\",\"answer\":\"The framework integrates machine learning models with metaheuristic optimization strategies and deep learning, using evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance model performance.\"},{\"question\":\"How does the study improve model interpretability and usability for healthcare professionals?\",\"answer\":\"Explainable AI (XAI) techniques are applied to improve interpretability, helping clinicians understand key predictive factors used by the model.\"}]","Optimized Machine Learning Models For Early Detection Of Alcohol Use Disorder - A Hybrid Approach | PDF",1785735173,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimized-machine-learning-models-for-early-detection-of-alcohol-use-disorder-a-hybrid-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimized-machine-learning-models-for-early-detection-of-alcohol-use-disorder-a-hybrid-approach/121345/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early and accurate detection of Alcohol Use Disorder (AUD) important?","Question",{"text":75,"@type":76},"AUD is a major global health issue where earlier identification enables timely intervention. Early detection also helps address inaccuracies and biases common in conventional assessments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed hybrid approach combine to improve AUD detection?",{"text":80,"@type":76},"The framework integrates machine learning models with metaheuristic optimization strategies and deep learning, using evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance model performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study improve model interpretability and usability for healthcare professionals?",{"text":84,"@type":76},"Explainable AI (XAI) techniques are applied to improve interpretability, helping clinicians understand key predictive factors used by the model.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]