International Journal of Science, Engineering and TechnologyAuthors: Shah Md. Tanzimul Kabir, Md. Yusuf Miah
Abstract: This paper provides a comprehensive analysis of the predictive modeling frameworks of Artificial Intelligence for smart decision support systems (DSS) and the evolution of the traditional analytics approach to an integrated Artificial Intelligence approach for real-time decision-making. Through a systematic study of the recent research articles from 2021 to 2026, the paper explores the advancements in machine learning models and hybrid Artificial Intelligence approaches for transforming the traditional decision support systems in the healthcare, finance, manufacturing, and environmental management domains. The research proposes an Integrated Predictive Decision Support Framework (IPDSF) that incorporates data preprocessing, model selection, explainability, and human validation for effective predictions. The study reveals that the contemporary Artificial Intelligence-based decision support systems employ ensemble learning (Random Forest and XGBoost with an accuracy rate of 89-96%), deep learning for complex pattern recognition (CNN for medical image analysis and LSTM for time series analysis), and hybrid neuro-symbolic models for effective predictions. Some challenges still exist in model interpretability, but Explainable AI (XAI) techniques such as SHAP and LIME have become a key component in building user trust and ensuring compliance. Comparative evaluation of AI-DSS along four analytical dimensions—model architecture, interpretability, real-time, and domain adaptation—clearly shows that an appropriate balance between predictive accuracy, interpretability, efficiency, and integration with existing decision processes is required.
DOI: https://doi.org/10.5281/zenodo.20270472
