BIG DATA ANALYSIS FOR DISEASE PREDICTION AND PREVENTION - Loso Judijanto; Lola Yustrisia; Entin Solihah (CV. ADIBA AISHA AMIRA, 2025)
Big Data analytics in the context of disease prediction and prevention stands out as a critical issue in today's digital age, given its unique capacity to process and analyze massive volumes of health data with unprecedented speed and precision. Through the collection of extensive data from various sources, including electronic medical records, wearable devices, and other digital inputs, Big Data analysis enables researchers and healthcare practitioners to identify patterns and trends before diseases develop, forecast outbreaks, and respond proactively to potential health crises. Its ability to integrate and map health data at scale opens up opportunities for smarter prevention and personalized approaches to disease management, significantly shifting the landscape of disease prevention from reactive to proactive, which in turn could save millions of lives and reduce the economic burden on the global health system. The study in this research uses the literature research method. The results show that the use of Big Data and machine learning has great potential in strengthening health systems through disease prediction and prevention. Key findings show that the integration of extensive health data enables more effective identification of disease patterns and trends. With these technologies in place, the ability to diagnose and forecast diseases becomes faster and more accurate, which in turn, can help in designing appropriate and evidence-based interventions. In addition, improved machine learning methods continue to push the boundaries of predictiveness, providing new insights into proactive disease management and prevention.