Smart Healthcare Analysis

Abstract: The healthcare sector is currently undergoing a paradigm shift from reactive treatment to proactive, predictive care, driven by the explosion of "Big Data" from Electronic Health Records (EHRs) and wearable devices. However, the integration of this data into daily clinical practice remains fragmented, leading to reliance on manual diagnostics that are error-prone and time-consuming. This paper presents the Smart Healthcare Analysis System, a web-based predictive modeling framework designed to bridge the gap between raw medical data and actionable clinical insights. The system introduces a five-stage architectural pipeline combining advanced data preprocessing (including SMOTE for class imbalance), robust machine learning classification (utilizing Random Forest and XGBoost), and explainable AI techniques. Evaluated against standard healthcare datasets, the system achieves a predictive accuracy of over 90% in disease risk assessment while providing real-time decision support (<2 seconds latency). Unique to this framework is the integration of Feature Importance Analysis, which enhances clinical trust by transparently visualizing the physiological parameters driving each prediction. This work offers a scalable, economically feasible solution for modernizing healthcare delivery, particularly in resource-constrained environments.

Keywords: Healthcare Analytics, Machine Learning, Predictive Modeling, Decision Support Systems, Explainable AI, Disease Prediction.