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This journal-style study integrates classical statistical models with modern ML methods to address prediction, classification, risk detection, and decision support in four applied domains. An illustrative multidisciplinary dataset compares regression, logistic regression, random forest, gradient boosting, and regularized learning. Results indicate that statistical models support interpretability and inference, while ML typically improves predictive performance under nonlinearities. Domain analyses link risk and outcomes to distinct feature groups.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multidisciplinary-applications-of-statistical-modelling-and-machine-learning-in-finance-health-education-and-society-journal-study/290712/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multidisciplinary-applications-of-statistical-modelling-and-machine-learning-in-finance-health-education-and-society-journal-study/290712.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the study combine statistical modelling with machine learning across applied domains?","Question",{"text":113,"@type":114},"It presents a framework that integrates classical statistical models with modern machine-learning methods to solve prediction, classification, risk detection, and decision-support tasks in finance, health, education, and social policy.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What models are compared in the analysis?",{"text":118,"@type":114},"The study compares regression and logistic regression with machine-learning approaches including random forest, gradient boosting, and regularized learning methods.",{"name":120,"@type":111,"acceptedAnswer":121},"What key trade-off does the paper highlight between statistical models and machine-learning models?",{"text":122,"@type":114},"Traditional statistical models are emphasized for interpretability, inference, and hypothesis testing, while machine-learning models generally yield stronger predictive performance when nonlinear relationships and interactions are present.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290712,1789642419,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Multidisciplinary Applications of Statistical Modelling and Machine Learning in Finance, Health, Education, and Society  \nPaul Pritom 1, Imran Chowdhury2  \n1 Department of Economics, Comilla University, Cumilla, Bangladesh  \n2 Department of Economics, Independent University, Bangladesh, Dhaka, Bangladesh  \nAbstract  \nStatistical modelling and machine learning are now central tools for evidence-based decision-making across finance, health, education, and social policy. This paper presents a multidisciplinary journal-style study showing how classical statistical models and modern machine-learning methods can be combined to analyze prediction, classification, risk detection, and decision-support problems across four applied domains. Using an illustrative multidisciplinary dataset, the study compares regression, logistic regression, random forest, gradient boosting, and regularized learning approaches. The findings show that traditional statistical models remain useful for interpretability, inference, and hypothesis testing, while machine-learning models generally provide stronger predictive performance when nonlinear relationships and interaction effects are present. Domain-specific results show that financial risk prediction depends on volatility, liquidity, inflation, and market return indicators; health classification is driven by biomarker, behavioral, and age-related features; education risk prediction is strongly linked to early assessment, learning engagement, and attendance; and social well-being analysis is associated with digital access, employment stability, education, and health access. The paper argues that multidisciplinary analytics should balance accuracy, interpretability, fairness, and practical actionability. The study contributes a structured framework for applying statistical and machine-learning methods responsibly across applied research settings.  \nKeywords: Statistical Modelling; Machine Learning; Finance; Health Analytics; Learning Analytics; Social Data Science; Predictive Modelling  \n1. Introduction  \nStatistical modelling and machine learning have become essential approaches for understanding complex patterns in data across finance, health, education, and society. Modern institutions generate large volumes of data through digital platforms, learning management systems, administrative records, financial markets, health systems, social databases, and online interactions. These data provide opportunities to identify risk, predict outcomes, evaluate interventions, and support evidencebased decision-making. In education, learning analytics and early-warning systems have been used to identify at-risk students before final academic failure occurs [1], [2] . These studies show that datadriven systems can support timely intervention when student activity, engagement, and assessment indicators are analyzed responsibly.  \nThe increasing use of machine learning also requires careful attention to fairness, interpretability, and model design. Algorithmic systems may improve prediction, but they may also reproduce bias if data are incomplete or historically unequal [3] . Classical machine-learning approaches such as random forests and gradient boosting are widely used because they can detect nonlinear patterns and improve classification accuracy [4], [5] . In health analytics, machine learning has been applied to diagnosis, clinical prediction, and patient risk classification [6],[7]. In finance, machine-learning methods have improved empirical asset pricing and portfolio-related applications by capturing complex relationships among financial indicators [8],[9] .  \nStatistical modelling and machine learning are complementary rather than competing approaches. Statistical models are valuable for estimation, inference, uncertainty measurement, and explanation, while machine-learning models are powerful for prediction, classification, and nonlinear pattern recognition [10] . In finance, deep learning and related approac","cbCaiv6Axl0BBgpP","https://ap.wps.com/l/cbCaiv6Axl0BBgpP","pdf",569055,15,"English","# Abstract\n# 1. Introduction\n## Evidence-based decision-making across domains\n## Fairness, interpretability, and model design\n## Complementarity of statistical and ML approaches\n## Relevance to education and social risk identification\n## Policy and decision research implications","[{\"question\":\"How does the study combine statistical modelling with machine learning across applied domains?\",\"answer\":\"It presents a framework that integrates classical statistical models with modern machine-learning methods to solve prediction, classification, risk detection, and decision-support tasks in finance, health, education, and social policy.\"},{\"question\":\"What models are compared in the analysis?\",\"answer\":\"The study compares regression and logistic regression with machine-learning approaches including random forest, gradient boosting, and regularized learning methods.\"},{\"question\":\"What key trade-off does the paper highlight between statistical models and machine-learning models?\",\"answer\":\"Traditional statistical models are emphasized for interpretability, inference, and hypothesis testing, while machine-learning models generally yield stronger predictive performance when nonlinear relationships and interactions are present.\"}]","Multidisciplinary Applications of Statistical Modelling and Machine Learning in Finance, Health, Education, and Society - Journal Study | PDF",38]