Predictive analytics frameworks have emerged as a critical component in modern healthcare systems for enabling early disease diagnosis using large-scale healthcare big data. These frameworks integrate advanced data mining techniques, machine learning algorithms, artificial intelligence models, and distributed computing systems to analyze massive and heterogeneous healthcare datasets. The data sources include electronic health records (EHRs), wearable sensors, medical imaging systems, genomic data, clinical reports, and real-time patient monitoring devices. By processing and analyzing these diverse datasets, predictive analytics systems can identify hidden patterns, detect anomalies, and forecast potential disease risks before symptoms become severe. This enables healthcare professionals to take preventive actions, improve diagnostic accuracy, and enhance patient outcomes. Common predictive models include logistic regression, decision trees, random forests, support vector machines, deep neural networks, and ensemble learning methods. Big data platforms such as Hadoop and Spark provide scalable processing capabilities, while cloud computing ensures flexible storage and computational power. Despite significant advantages such as early detection, personalized medicine, and cost reduction, challenges such as data privacy, interoperability, data quality, model interpretability, and computational complexity persist. This study explores predictive analytics frameworks, literature contributions, methodologies, advantages, and limitations in healthcare big data systems for early disease diagnosis.
