Precision Care Navigator: Predictive Analytics for Patient-Centered Healthcare (PCN-Patch)

Developing analytics for hospitals' healthcare data to optimize patient care, streamline operations, and enhance overall efficiency is a crucial endeavor. However, existing systems often face challenges in accessing the right data in the correct format for analysis and struggle with accurately forecasting disease progression and patient readmission risks. To address these issues, we propose a hybrid machine learning algorithm designed to accurately predict disease progression and assess the risk of readmission. This innovative tool focuses on ST elevation myocardial infarction (STEMI), a cardiac condition requiring meticulous monitoring. The architecture predicts the progression level of STEMI using Logistic regression, yielding values from 0 to 10. The Risk Prediction component, employing a Neuro-Fuzzy algorithm, accurately categorizes risk into low, medium, high, and very high. This comprehensive approach aims to improve patient outcomes, optimize resource allocation, and enhance overall healthcare efficiency.