AN INTELLIGENT HEALTHCARE PREDICTIVE ANALYTICS SYSTEM FOR DISEASE RISK PREDICTION USING NLP, DATA MINING, AND EXPLAINABLE AI

Abstract

The rapid digitization of healthcare systems has resulted in the generation of massive volumes of clinical data, ranging from structured physiological measurements to unstructured clinical narratives. In Traditional methods of manual analysis are no longer sufficient to process this data for early disease detection and proactive intervention. This research proposes an integrated intelligent healthcare predictive analytics system that synthesizes Natural Language Processing, data mining techniques, machine learning classifiers, and explainable artificial intelligence.  The framework is designed to ingest multi-modal data from electronic health records, applying sophisticated feature extraction methods such as TF-IDF and transformer-based embeddings to clinical notes while normalizing structured vital signs. In Multiple classification models, including Random Forest, Support Vector Machines, and Logistic Regression, are evaluated for their predictive performance across various chronic conditions. To bridge the gap between model accuracy and clinical trust, the system incorporates SHAP-based interpretability layers to provide transparent, feature-level justifications for each prediction. Empirical evaluations demonstrate that this hybrid approach significantly enhances diagnostic precision and provides actionable insights for clinical decision support. By mitigating the inherent limitations of conventional black-box algorithms, this architecture fosters greater practitioner confidence, ensuring that automated risk assessments are not only accurate but also inherently interpretable and clinically validated. This paradigm shift facilitates seamless integration into existing clinical workflows, enabling healthcare professionals to make well-informed, evidence-based decisions while maintaining full oversight of the predictive rationale. Ultimately, the system bridges the critical gap between computational sophistication and practical utility, paving the way for safer, more reliable AI deployment in high-stakes medical environments.