A Scalable Framework for Big Data Analytics in Psychological Research: Leveraging Distributed Systems and Cluster Management

Ogur N.B. Ceken C. Selim Ogur Y. Yazici E.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2025 #13 174947 - 174956 pp.

Anxiety and depression are prevalent psychological disorders that can occur throughout life, with a notably higher prevalence among women during the perinatal period, encompassing pregnancy and the postpartum phase. The early detection and monitoring of these conditions are crucial for timely intervention and improved patient outcomes. Although healthcare analytics has progressed considerably, the extraction of actionable insights from large-scale patient data remains computationally intensive, especially under instant processing constraints. Furthermore, conventional healthcare infrastructures frequently lack the scalability, computational efficiency, and architectural flexibility required to integrate machine learning models into clinical workflows effectively. To address these challenges, the proposed distributed computing framework employs Apache Kafka for instant data streaming, Apache Spark for efficient in-memory machine learning–based analytics, and Kubernetes for orchestrating scalable, fault-tolerant deployment. This architectural configuration facilitates continuous data ingestion, accelerates analytical processing, and ensures system resilience, thereby enabling the timely identification of psychological conditions such as anxiety and depression during the perinatal period. Unlike schematic or batch-only prior work, we provide a production-ready, streaming-first clinical deployment and an empirical scaling analysis linking executors to end-to-end diagnostic latency and resource efficiency. Performance evaluations demonstrate the efficiency and scalability of the proposed system, highlighting its potential for real-world applications in healthcare analytics.

Apache Spark , Docker containerization , high-performance computing (HPC) , Kubernetes orchestration , perinatal psychological disorders , Real-time data analytics

Text of the article Перейти на текст статьи

Sakarya University, Faculty of Computer and Information Sciences, Department of Software Engineering, Sakarya, 54050, Turkey
Sakarya University, Faculty of Computer and Information Sciences, Department of Computer Engineering, Sakarya, 54050, Turkey
North Kazakhstan University, International Campus, Petropavlovsk, 150000, Kazakhstan
Serdivan State Hospital, Department of Psychiatry, Sakarya, 54050, Turkey
Sakarya University, Faculty of Medicine, Department of Psychiatry, Sakarya, 54050, Turkey

Sakarya University
Sakarya University
North Kazakhstan University
Serdivan State Hospital
Sakarya University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026