[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124524-en":3,"doc-seo-124524-105":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},124524,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning Model for Assessing Human Well-being using Brain Wave Activities","Study proposes a machine learning method to assess human well-being by analyzing brain wave activities. A Random Forest classifier is trained to assign EEG-derived patterns to three well-being states—good, normal, and bad—using synthetic data that simulate electroencephalography readings. The model reaches overall accuracy of 96.17%. Feature importance indicates alpha waves (34%) and beta waves (29%) as key predictors. ROC analysis yields AUC values from 0.984 to 0.999, supporting strong class discrimination while noting the need for validation with real EEG recordings.","Journal of Informatics and Web Engineering  \nVol. 4 No. 2 (June 2025) eISSN: 2821-370X  \nMachine Learning Model for Assessing Human Well-being using Brain Wave Activities  \nSellappan Palaniappan1*, Rajasvaran Logeswaran2, Yong Yoke Leng3  \n1Corporate, HELP University, No. 15, Jalan Sri Semantan 1, Off Jalan Semantan, Bukit Damansara 50490 Kuala Lumpur,  \nWilayah Persekutuan, Malaysia  \n2,3Faculty of Computing and Digital Technology, HELP University, Persiaran Cakerawala, Subang Bestari, 40150 Shah Alam, Selangor, Malaysia  \n*corresponding author: ([sellappan.p@help.edu.my](sellappan.p@help.edu.my); ORCiD: 0009-0009-1168-2864)  \nAbstract-This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength in distinguishing between optimal and suboptimal well-being states, with no misclassifications between these extremes. ROC curve analysis further confirmed excellent discriminative ability across all three classes, with AUC values ranging from 0.984 to 0.999. The study demonstrates the potential of machine learning in interpreting complex neurophysiological data for personalised health monitoring, potentially enabling real-time assessment and intervention strategies. While promising, the use of synthetic data necessitates further validation with real-world EEG recordings. This research contributes to the growing field of computational neuroscience and its applications in mental health and well-being assessment, potentially paving the way for more objective and personalised mental health interventions. Future directions include incorporating temporal dynamics, accounting for individual variability, and integrating multiple data sources for a more holistic approach to well-being assessment.  \nKeywords—Machine Learning, Human Well-being, Brain Wave Activities, EEG Analysis, Computational Neuroscience  \nReceived:3 September 2024; Accepted: 10 March 2025; Published:16 June 2025 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nHuman well-being is a multifaceted construct encompassing physical, mental, emotional, and social health. It represents a holistic state of being that reflects an individual's overall quality of life and satisfaction across various domains. Accurate assessment of well-being is crucial for developing effective health monitoring and intervention strategies, particularly in an era where mental health concerns are increasingly prevalent [1] .  \nWell-being has been defined in a much more comprehensive way than it was in the past, and it is not constrained within the framework of the medical model which has a rather narrow perception of well-being as the absence of illness and incorporates psychological, social and even environmental factors. This change has led to the recognition of mental health as one of the central aspects of well-being. The World Health Organization has the following to say about mental health: “A healthy state, where an individual is able to deal with the pressures of everyday life, is able to work productively, and is able to help the society” [1] .  \nThe connection between brain activity and general well-being can now be better understood because of recent developments in machine learning and neuroscience. Due to these technological advancements, a more nuanced and objective evaluation of mental states can be cond","cbCaimeYGOY30QKg","https://ap.wps.com/l/cbCaimeYGOY30QKg","pdf",642297,1,21,"English","en",105,"# Abstract\n# Introduction\n## Well-being as a multifaceted construct\n## Brain activity and machine learning\n## EEG and its role in assessment\n## Advantages of EEG over other neuroimaging methods","[{\"question\":\"What method does the study use to assess human well-being?\",\"answer\":\"It uses a Random Forest machine learning classifier trained on EEG-derived brain wave activity to categorize well-being into three states: good, normal, and bad.\"},{\"question\":\"How well does the model perform, and what metrics support this?\",\"answer\":\"The study reports 96.17% overall accuracy and ROC analysis with AUC values ranging from 0.984 to 0.999, indicating strong discriminative ability across all three classes.\"},{\"question\":\"Which brain wave features are most important for predicting well-being?\",\"answer\":\"Feature importance analysis shows alpha waves (34%) and beta waves (29%) as the most significant predictors of well-being state.\"}]","Machine Learning Model for Assessing Human Well-being using Brain Wave Activities | 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method does the study use to assess human well-being?","Question",{"text":75,"@type":76},"It uses a Random Forest machine learning classifier trained on EEG-derived brain wave activity to categorize well-being into three states: good, normal, and bad.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How well does the model perform, and what metrics support this?",{"text":80,"@type":76},"The study reports 96.17% overall accuracy and ROC analysis with AUC values ranging from 0.984 to 0.999, indicating strong discriminative ability across all three classes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which brain wave features are most important for predicting well-being?",{"text":84,"@type":76},"Feature importance analysis shows alpha waves (34%) and beta waves (29%) as the most significant predictors of well-being 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