Real-Time Diabetes Detection Using Machine Learning and Apache Spark

The convergence of contemporary and cutting-edge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. This work presents distinct approaches and contributions, addressing specific challenges in healthcare and disease and diabetes analytics. The real-time healthcare for disease diabetes and the integration of big data analytics, machine learning, and real-time processing have paved the way for innovative solutions to address disease diabetes prediction and monitoring. It’s explored innovative solutions to overcome challenges in healthcare analytics, offering real-time predictions and continuous monitoring for improved patient care. The real-time Healthcare-Diabetes dataset was analyzed using various machine learning models, and processing in real-time has led to innovative solutions to address diabetes prediction and monitoring. The Gradient Boosted Tree Classifier emerged as the most accurate model with an accuracy of 90.14%, followed by the Decision Tree Classifier at 84.62%, the Random Forest Classifier at 82.84%, the Linear Support Vector Classifier at 78.70%, and Logistic Regression at 64.69%. These results demonstrate the system’s robustness and efficiency in real-time data collection, processing, and prediction. Leveraging Apache Spark and open-source big data technologies, specify data challenges and advocate for scalable, efficient, and cost-effective healthcare analytics. It contributes to the ongoing transformation of healthcare systems, demonstrating the effectiveness of advanced technologies in enhancing disease prediction, monitoring, and overall healthcare services.