[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123532-en":3,"doc-seo-123532-105":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},123532,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets - Abstract","Alzheimer’s disease (AD) is a leading neurodegenerative disorder whose early diagnosis remains difficult due to complex pathology driven by neurofibrillary tangles and amyloid plaques. Although genetic mechanisms are increasingly accessible through data mining, machine learning, and microarray technologies, high-dimensional gene expression data create the curse of dimensionality, leading to overfitting, bias, and high computational cost. This study introduces an enhanced manta ray foraging optimizer gene-selection framework with Sign Random Mutation and Best Rank improvements to strengthen exploration and exploitation. Using six datasets and four classifiers, the method identifies relevant genes and improves prediction accuracy.","Submitted 17 April 2025  \nAccepted 1 July 2025  \nPublished 14 August 2025  \nCorresponding author Zahraa Ahmed, [203720153@ogr.altinbas.edu.tr](203720153@ogr.altinbas.edu.tr)  \nAcademic editor Jiayan Zhou  \nAdditional Information and Declarations can be found on page 32  \nDOI 10.7717/peerj-cs.3064  Copyright  \n2025 Ahmed and Çevik Distributed under  \nCreative Commons CC-BY 4.0  \nImproving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets  \nZahraa Ahmed and Mesut Çevik  \nDepartment of Electrical and Computer Engineering, Altınbaş Üniversitesi, Istanbul, Turkey  \nABSTRACT  \nOne of the most prominent neurodegenerative diseases globally is Alzheimer’s disease (AD) . The early diagnosis of AD is a challenging task due to complex pathophysiology caused by the presence and accumulation of neurofibrillary tangles and amyloid plaques. However, the late enriched understanding of the genetic underpinnings of AD has been made possible due to recent advancements in data mining analysis methods, machine learning, and microarray technologies. However, the “curse of dimensionality” caused by the high-dimensional microarray datasets impacts the accurate prediction of the disease due to issues of overfitting, bias, and high computational demands. To alleviate such an effect, this study proposes a geneselection approach based on the parameter-free and large-scale manta ray foraging optimization algorithm. Given the dimensional disparities and statistical relationship distributions of the six investigated datasets, in addition to four evaluated machine learning classifiers; the proposed Sign Random Mutation and Best Rank enhancements that substantially improved MRFO’s exploration and exploitation contributed to efficient identification of relevant genes and to machine learning improved prediction accuracy.  \nSubjects Artificial Intelligence, Computer Vision, Data Mining and Machine Learning, Optimization Theory and Computation  \nKeywords Alzheimer’s disease, Gene selection, Manta ray foraging optimizer (MRFO), Sign random mutation, Best rank, Dimensionality reduction  \nINTRODUCTION  \nMedical research has identified Alzheimer’s disease (AD), also known as senile dementia, as the most common type of neurodegenerative disease that significantly impairs patient’s capacity to perform daily activities. The development of intracellular neurofibrillary tangles due to tau hyperphosphorylation, the loss of neuronal tissue caused by gliosis proliferation, and the formation of extracellular amyloid plaques due to aberrant amyloid beta accumulation are among the pathological characteristic abnormalities of AD (Hüttenrauch et al., 2018) . Reactive astrocyte morphology describes the molecular alterations that cause the brain cells to exhibit significant morphological changes in response to stressful conditions (Preman et al., 2021) . In a healthy brain, astrocytes play several roles, such as promoting neuronal metabolism, maintaining the blood-brain barrier’s integrity, and maintaining the equilibrium of ions in the extracellular space  \nHow to cite this article Ahmed Z, Çevik M. 2025. Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets. PeerJ Comput. Sci. 11:e3064 DOI 10.7717/peerj-cs.3064  \n(Siracusa, Roberta & Salvatore, 2019; Vasile, Elena & Nathalie, 2017) . Despite its high prevalence, Alzheimer’s disease is still one of most widely researched disease (Holtzman, John & Alison, 2011; Rocca & Luigi, 2019). In recent years, the field of molecular biology witnessed significant advancements due to integration of data mining and machine learning techniques in microarray analysis, and particularly in the diagnosis and treatment of molecular diseases. These computation approaches—such as rule mining, featureselection, clustering analysis, machine and deep learning—assist the identification of complex and intricate patte","cbCailQB2szz028i","https://ap.wps.com/l/cbCailQB2szz028i","pdf",10925220,1,35,"English","en",105,"# Abstract\n## Introduction\n## Problem: challenges of high-dimensional microarray data\n## Motivation: feature selection and dimensionality reduction","[{\"question\":\"Why is early Alzheimer’s disease diagnosis challenging?\",\"answer\":\"Early diagnosis is difficult because AD involves complex pathological processes, including neurofibrillary tangles and amyloid plaques, which complicate reliable detection.\"},{\"question\":\"What problem does high-dimensional microarray data cause for machine learning models?\",\"answer\":\"High dimensionality leads to the curse of dimensionality, increasing overfitting risk, bias, and computational demands while reducing predictive accuracy and interpretability.\"},{\"question\":\"How does the proposed method improve gene selection and prediction?\",\"answer\":\"The study enhances a manta ray foraging optimization approach by adding Sign Random Mutation and Best Rank updates, improving exploration and exploitation so the model selects relevant genes and achieves better prediction accuracy.\"}]","Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets - 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