Machine Learning Techniques for Automated Decision Support Systems

Samuel Kaski

Machine Learning Techniques for Automated Decision Support Systems

Author(s)	Samuel Kaski
Country	Finland
Abstract	Machine learning has become an important technological foundation for automated decision support systems because of its ability to identify patterns, generate predictions, classify complex information, and support data-driven decisions. Traditional decision support systems frequently depend on predefined rules and manually specified relationships, whereas machine learning-based systems can learn decision patterns from historical and continuously generated data. This study examines machine learning techniques used for automated decision support systems, with particular emphasis on predictive modeling, classification, adaptive learning, intelligent recommendation, and explainable decision support. A qualitative descriptive research design was adopted through structured analysis of scholarly literature concerning machine learning and decision support applications. Existing research indicates that machine learning-based decision support is being investigated across healthcare, business, education, finance, manufacturing, cybersecurity, and other application domains. Recent systematic reviews show that machine-learning decision support systems can assist with tasks such as risk assessment, image interpretation, prediction, and classification, although their effects on human decision performance can vary considerably across contexts. The findings further indicate that accuracy alone is insufficient for evaluating automated decision support. Explainability, reliability, usability, human oversight, fairness, and integration with organizational workflows are equally important. Recent research on explainable decision support emphasizes that transparency can improve user understanding and confidence, while poorly designed explanations may increase cognitive burden. The study concludes that future automated decision support systems should combine accurate machine learning models with transparent interfaces, adaptive capabilities, strong validation procedures, and meaningful human involvement.
Keywords	Machine Learning, Automated Decision Support, Artificial Intelligence, Predictive Analytics, Intelligent Systems, Decision Automation, Explainable AI, Data-Driven Decision-Making, Classification, Predictive Modeling
Field	Engineering
Published In	Volume 1, Issue 3, May-June 2019
Published On	2019-06-29

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Techniques for Automated Decision Support Systems

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