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September 10, 2025INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Scalable Data Mining Algorithms for Real-Time Analysis of Big Data Streams in Healthcare

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AKAnil KumarCentral University of Punjab

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Implication

This paper demonstrates real-time analytics for high-velocity healthcare data, suggesting scalable solutions using Apache Spark and incremental learning.

Key Points

The proposed system efficiently analyzes big data streams in healthcare, achieving up to 2,000 events per second.
Key algorithms like streaming decision trees and online logistic regression enable dynamic classification and anomaly detection in real time.
Utilizing Apache Spark allows in-memory processing for low-latency performance, maintaining efficiency under varying workloads.
Challenges such as concept drift and patient data privacy in real-time analytics are critically addressed, showcasing practical applications.

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Anil Kumar (2022) studied this question.

synapsesocial.com/papers/68c1e30154b1d3bfb6100361 https://doi.org/10.55041/ijsrem11454

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Implication

This paper demonstrates real-time analytics for high-velocity healthcare data, suggesting scalable solutions using Apache Spark and incremental learning.

Scalable Data Mining Algorithms for Real-Time Analysis of Big Data Streams in Healthcare

Discussion

Member takes

Implication

Key Points

Cite This Study

Scalable Data Mining Algorithms for Real-Time Analysis of Big Data Streams in Healthcare

Discussion

Member takes

Implication

Key Points

Cite This Study

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