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Free Download A Parallel Patient Treatment Time Prediction Algorithm Using Bigdata Project in Java with source code

Java project     kalai selvi    2020-01-05

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A Parallel Patient Treatment Time Prediction Algorithm Using Bigdata project is a desktop application which is developed in Java platform. This Java project with tutorial and guide for developing a code. A Parallel Patient Treatment Time Prediction Algorithm Using Bigdata is a open source you can Download zip and edit as per you need. If you want more latest Java projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into virtual box hadoop. This Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering final year students can submit in college. This script developed by kalai selvi. This desktop application 100% working smooth without any bug. It is developed using hadoop and Database hdfs. This software code helpful in academic projects for final year students. We have a great collection of Java projects.





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This doucment file contains project Synopsis, Reports, and various diagrams. Also abstract pdf file inside zip so that document link below the page. Entity–relationship(ER) diagrams, Data flow diagram(DFD), Sequence diagram and software requirements specification (SRS) in report file. Complete ready made open source code free of cost download. You can find Top Downloaded Java projects here.

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About Project



project Name :  A Parallel Patient Treatment Time Prediction Algorithm Using Bigdata
project ID :  2313
Developer Name :  kalai selvi
Upload Date :  2020-01-05
project Platform :  Java
Programming Language :  hadoop
IDE Tool :  virtual box hadoop
project Earning :  kalai selvi Earn Rs.150 from this project.
Database :  hdfs
project Type :  desktop Application
No of project Download :  52
project Total View :  874
Today Trends :  118
Current Month Trends :  126
Last Month Trends :  36
project Source Code Link :   Download Here (3.3372MB)
project Report Link :   Download here  (2.5172MB)

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Features of the Project

Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efficient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efficiency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Centre in Changsha to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals.


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