Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
A health recommendation system using machine learning, built on Hadoop, Spark, and HDFS, represents a significant advancement in personalized healthcare. This project aims to leverage big data technologies to process and analyze vast amounts of medical data across a distributed computing environment, utilizing at least three virtual machines. The background of this project lies in the increasing prevalence of chronic diseases and the growing volume of health-related data collected by healthcare providers. The motivation for this project stems from several key factors. Firstly, traditional healthcare systems often struggle to provide personalized recommendations due to the sheer volume and complexity of medical data. By utilizing Hadoop and Spark’s distributed processing capabilities, this system can efficiently analyze large-scale health data, enabling more accurate and timely recommendations. Secondly, the integration of machine learning algorithms with big data technologies allows for the identification of subtle patterns and correlations in patient data that may not be apparent through conventional analysis methods. This can lead to more precise diagnoses and treatment plans tailored to individual patients. The expected results of this project include a robust health recommendation system capable of processing and analyzing large volumes of medical data in a distributed environment.
Included in
GC-058 Personalized Wellness Recommendations
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
A health recommendation system using machine learning, built on Hadoop, Spark, and HDFS, represents a significant advancement in personalized healthcare. This project aims to leverage big data technologies to process and analyze vast amounts of medical data across a distributed computing environment, utilizing at least three virtual machines. The background of this project lies in the increasing prevalence of chronic diseases and the growing volume of health-related data collected by healthcare providers. The motivation for this project stems from several key factors. Firstly, traditional healthcare systems often struggle to provide personalized recommendations due to the sheer volume and complexity of medical data. By utilizing Hadoop and Spark’s distributed processing capabilities, this system can efficiently analyze large-scale health data, enabling more accurate and timely recommendations. Secondly, the integration of machine learning algorithms with big data technologies allows for the identification of subtle patterns and correlations in patient data that may not be apparent through conventional analysis methods. This can lead to more precise diagnoses and treatment plans tailored to individual patients. The expected results of this project include a robust health recommendation system capable of processing and analyzing large volumes of medical data in a distributed environment.
