Machine Learning Models for Intelligent Decision Support Systems

Abstract

Machine Learning (ML) models are playing a transformative role in the development of Intelligent Decision Support Systems (IDSS), offering innovative approaches to data-driven decision-making processes across various domains. These systems leverage ML techniques to analyze complex datasets, uncover hidden patterns, and provide decision-makers with actionable insights. The integration of ML into IDSS enhances predictive analytics, optimizes decision-making, and improves the efficiency and accuracy of critical business and operational processes. Key machine learning algorithms, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, have shown promising applications in diverse sectors, including healthcare, finance, supply chain management, and smart cities. The ability to process large-scale, unstructured data, such as text, images, and sensor data, allows IDSS to support real-time and adaptive decision-making. Moreover, the incorporation of explainable AI techniques has become crucial for ensuring transparency and trust in the recommendations provided by these systems. Despite the significant advancements, challenges such as data quality, model interpretability, and integration with existing systems still pose barriers to the widespread adoption of ML-based IDSS. This paper explores the various ML techniques used in IDSS, discusses their applications, and identifies the key challenges and future research directions in the field. The findings provide valuable insights for researchers and practitioners aiming to design and implement more effective and intelligent decision support systems.