AI-Driven Predictive Analytics for Smart Healthcare Systems
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Abstract
This paper explores the integration of artificial intelligence in predictive healthcare systems to enhance early diagnosis and patient outcomes. By leveraging machine learning algorithms and real-time patient data from IoT-enabled medical devices, the proposed framework predicts potential health risks with high accuracy. The study evaluates various models such as Random Forest and Deep Neural Networks on clinical datasets. Results demonstrate improved disease prediction rates and reduced hospitalization risks. The research highlights the role of AI in transforming traditional healthcare into a proactive and data-driven ecosystem.
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References
Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1–13.
Jiwani, N., Gupta, K., & Whig, P. (2021). Novel healthcare framework for cardiac arrest with the application of AI using ANN. In Proceedings of the 5th International Conference on Information Systems and Computer Engineering.
Nadikattu, R. R., Mohammad, S. M., & Whig, P. (2020). Novel economical social distancing smart device for COVID-19. International Journal of Electrical Engineering and Technology (IJEET).
Pulivarthy, P., & Whig, P. (2025). Bias and fairness: Addressing discrimination in AI systems. In Ethical dimensions of AI development (pp. 103–126).
Jupalle, H., Kouser, S., Bhatia, A. B., Alam, N., Nadikattu, R. R., & Whig, P. (2022). Automation of human behaviors and its prediction using machine learning. Microsystem Technologies, 28(8), 1879–1887.
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (n.d.). Bone cancer classification and detection using machine learning technique. In Diagnosing musculoskeletal conditions using artificial intelligence and machine learning.