Predictive Analytics and Machine Learning Applications for Enhancing Decision Making in Healthcare and Financial Systems
Authors: Emekwisia, E. U., Adebanjo, T. A., Chizoba, C. E., Ofuegbe, S. B., Amosu, F. R., Tytler, T. D.
Journal: International Journal of Strategic Management and Business Policy (IJSMBP), ISSN 3023-3623
Citation: IJSMBP 7(2): 1-7, 2025-09-10.
DOI: 10.5281/zenodo.17091162
PDF: Download full-text PDF
Type: Original Research
Abstract
Artificial Intelligence and predictive analytics are reshaping decision-making processes in critical domains such as healthcare and finance. This study aims to evaluate and compare the performance of various machine learning models in enhancing predictive accuracy for medical diagnoses and financial forecasting. Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting were implemented on two benchmark datasets: the Heart Disease UCI dataset for healthcare and a stock price dataset for financial analysis. Performance metrics included accuracy, precision, recall, F1-score, and RMSE. In healthcare prediction, Random Forest achieved the highest accuracy at 91.4%, while Gradient Boosting recorded the lowest RMSE (4.7) in financial forecasting. These findings highlight the potential of predictive analytics in improving early diagnosis, treatment planning, and financial investment decisions, encouraging further deployment of explainable and scalable AI systems across industries.
Keywords
Artificial Intelligence, Predictive Analytics, Machine Learning Models, Healthcare Prediction, Financial Forecasting