AI-Powered Predictive Analytics in Healthcare: Revolutionizing Disease Diagnosis and Treatment
Keywords:
Clinical decision support, Deep learning, Electronic health records, Healthcare AI, Predictive analyticsAbstract
The advent of artificial intelligence (AI) in predictive healthcare analytics marks a pivotal evolution in modern medicine. By harnessing large-scale patient data from electronic health records (EHRs), medical imaging, genomic databases, and wearable devices, AI algorithms can identify hidden patterns, assess risks, and enable early disease diagnosis. This research paper explores three AI models tailored for predictive healthcare tasks: CNNs for X-ray-based pneumonia detection, BERT for analyzing clinical notes to assess diabetes risk, and XGBoost for evaluating hospital readmission probabilities. Our findings show CNNs achieved over 93% accuracy in image-based diagnosis, BERT exceeded 91% for text classification, and XGBoost reliably predicted readmission risks with 88% accuracy. We propose an integrated clinical decision support system (CDSS) framework that enhances interpretability, ensures privacy through federated learning, and enables adaptive, ethical deployment. This work demonstrates AI’s transformative role in improving diagnostic precision, personalizing care, and supporting medical professionals in decision-making.
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