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Predictive Analytics Models for Effective Decision Support Systems

Author(s) Guillaume Perrin
Country France
Abstract The increasing complexity of organizational environments has created a growing demand for decision support systems capable of transforming large volumes of information into reliable and timely recommendations. Conventional decision-making approaches often depend on historical reports, predefined rules, and manual interpretation, which may limit their effectiveness in rapidly changing environments. Predictive analytics provides advanced capabilities for identifying patterns, forecasting future outcomes, estimating risks, and supporting evidence-based decisions.
This study examines predictive analytics models for effective decision support systems and develops a conceptual framework integrating data acquisition, preprocessing, exploratory analysis, predictive modeling, model evaluation, decision interpretation, and continuous feedback. A conceptual and descriptive research design is adopted through the synthesis of secondary scholarly literature related to predictive analytics, machine learning, data mining, business intelligence, decision support systems, and intelligent computing.
The analysis indicates that predictive analytics can improve decision accuracy, forecasting capability, risk identification, resource allocation, operational efficiency, and strategic planning. However, data quality, model reliability, interpretability, computational complexity, bias, privacy, and changing environmental conditions remain important challenges. The proposed framework emphasizes the integration of predictive models with decision-support mechanisms rather than treating prediction as an independent analytical activity. The study concludes that appropriately designed predictive analytics models can strengthen decision support systems by providing timely, data-driven, and adaptable insights across organizational and research environments.
Keywords Predictive Analytics, Decision Support Systems, Machine Learning, Forecasting, Data Analytics, Decision Intelligence, Predictive Modeling, Business Intelligence, Data-Driven Decision-Making.
Field Engineering
Published In Volume 1, Issue 6, November-December 2019
Published On 2019-11-28
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