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@article{205699,
        author = {Dr D P Singh and no},
        title = {Matrix-Based Decision Analytics and Predictive Modeling in Management and Healthcare Systems Using Python},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7307-7321},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205699},
        abstract = {This research introduces a matrix-oriented framework for decision analytics and predictive modeling in management and healthcare applications using Python. The proposed methodology integrates matrix computations, weighted decision-analysis techniques, regression-based prediction models, risk evaluation approaches, and classification methods to enable effective data-driven decision support.
Within the management sector, the proposed framework is employed for employee performance appraisal, investment project prioritization, and supplier selection in supply chain management. Employee performance is assessed using weighted matrix-based computations integrated with regression-oriented predictive techniques, whereas investment alternatives and vendors are evaluated and ranked through weighted scoring mechanisms and risk-adjusted decision models. In the healthcare domain, matrix-driven predictive methodologies are applied to estimate cardiovascular disease risk and forecast hospital readmission probabilities. By integrating clinical attribute matrices with weighted predictive parameters, the framework produces individualized risk scores that support patient stratification, efficient resource allocation, and the identification of high-risk patients who require closer supervision and targeted intervention.
The effectiveness of the predictive models is measured using statistical performance indicators such as Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). In addition, classification performance is evaluated through metrics including accuracy, precision, recall, specificity, and F1-score. The entire analytical framework is implemented in Python, utilizing libraries such as NumPy, Pandas, Scikit-Learn, and Matplotlib for data management, predictive modeling, computational analysis, and graphical visualization. The findings demonstrate that matrix-based decision analytics provides an interpretable, scalable, and computationally efficient solution for complex management and healthcare problems. The framework enhances prediction accuracy, supports optimal resource utilization, and facilitates intelligent organizational and clinical decision-making, highlighting the practical value of matrix-driven approaches in modern analytical environments.},
        keywords = {Matrix Analytics; Decision Support Systems; Predictive Modeling; Management Analytics; Healthcare Analytics, Multi-Criteria Decision Making, Employee Performance Evaluation, Supply Chain Optimization, Healthcare Analytics, Disease Risk Prediction, Hospital Readmission Prediction, Machine Learning, Python.},
        month = {June},
        }