Predictive Analytics in Healthcare: Leveraging Machine Learning for Improved Patient Outcomes

• Lisa Chambugong

  Author

• Sarmin Akter

  Author

• Md Anwarul Matin Jony

  Author

• Masrufa Tasnim

  Author

• Md. Shihab Hossain

  Author

The advent of predictive analytics is reshaping healthcare more and more, using machine-learning methods to turn big data complexity into clinical relevance. In the US, healthcare organizations are under constant pressure to reduce spending and work with limited resources while providing successful and timely interventions. By predicting how the disease will progress, helping to identify patients at high-risk of developing a given outcome, fostering personalized treatment, and enhancing preventive care, predictive models can help meet these challenges. Goals: This review investigates the role of classification, clustering, and deep-learning approaches in improving diagnostic accuracy, patient management, and clinical decision-making. Practical applications and case studies provide concrete evidence for the effectiveness of machine-learning models to predict hospital readmissions, enhance chronic disease management, and direct timely treatment. Additionally, it addresses the core ethical and regulatory issues, such as concerns about the open nature of data protection and algorithm transparency, including HIPAA compliance and attention to detail. In conclusion, the results suggest that predictive analytics with appropriate use improves patient outcomes, resource utilization, and helps drive a more efficient, effective, and personalized healthcare system in the United States.

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