The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real‐time data analytics architecture for an IoT‐based smart healthcare system, which consists of a wireless sensor network and a radio‐frequency identification technology in a vertical domain. The proposed platform also includes high‐performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff–Parkinson–White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real‐time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT‐enabling technology. Therefore, the proposed architecture can be used as a time‐saving experimental environment for any IoT‐based system.

One of the forces driving science and industry is machine learning, but the proliferation of Big Data necessitates paradigm shifts from conventional approaches in applying machine learning techniques to this massive amount of data with varying velocity. Computers are now capable of accurately diagnosing a variety of medical conditions thanks to the availability of immense healthcare datasets and advancements in machine learning techniques. The study’s primary aim is to identify the most compelling questions on anxiety and depression in pregnant women by extracting features through performance-optimized algorithms. In this way, it is aimed to reach the result in a shorter time with fewer questions. The next goal of this work is to create an instant remote health status prediction system for depression and anxiety in pregnant women based on the Apache Spark Big Data processing engine, which concentrates on using machine learning models on streaming Big Data. In this scalable system, the application receives data from pregnant women to forecast the patient’s health condition. It then applies the machine learning algorithm that produces the best results for this dataset. With the assistance of this big data platform, the time-consuming anxiety and depression detection procedure in a pregnant woman can be replaced with a computer-based technique that works in an instant with a respectable amount of accuracy.

Introduction: Schizophrenia is a severe mental disorder affecting approximately 1% of the general population, diagnosed primarily using clinical criteria. Due to the lack of objective diagnostic methods and reliable biomarkers, accurate diagnosis and effective treatment remain challenging. Peripheral blood biomarkers have recently attracted attention, and machine learning methods offer promising analytical capabilities to enhance diagnostic accuracy.Methods: This retrospective, case-control study included 203 schizophrenia patients treated over a five-year period at a tertiary hospital and 192 age- and sex-matched healthy controls. Demographic data and routine hematological and biochemical parameters were extracted from medical records. Variables missing more than 85% of data were excluded; remaining missing values were imputed after train-test splitting to avoid data leakage. Optimal biomarker subsets were selected using Grey Wolf Optimization (GWO). Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression (LR) models were trained and evaluated via stratified 10-fold cross-validation.Results: Groups were homogeneous in terms of age and sex. Before GWO optimization, XGBoost (95.55%) and Random Forest (94.63%) yielded the highest accuracies. Following optimization, Random Forest accuracy improved (94.95%) with a recall of 96.25%, while XGBoost reached the highest accuracy (95.90%) and strong specificity (95.54%). Post-optimization, Area Under the Curve (AUC) values were highest for XGBoost (0.96) and Random Forest (0.95), indicating strong diagnostic performance. Total protein, glucose, iron, creatine kinase, total bilirubin, uric acid, calcium, and sodium were key biomarkers distinguishing schizophrenia. Interestingly, glucose levels were significantly lower in schizophrenia patients compared to controls, contrary to typical findings. Differences in triglycerides, liver enzymes, sodium, and potassium lacked clear clinical significance.Discussion: The machine learning models developed provided diagnostic accuracy comparable to studies utilizing more expensive biomarkers, highlighting potential clinical and economic advantages. External validation is recommended to further confirm the generalizability and clinical utility of these findings.

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.