A Parallel Patient Treatment Time Prediction Algorithm and its Applications in Hospital Queuing-Recommendation in a Big Data Environment

Abstract

A Parallel Patient Treatment Time Prediction big data project report Successful patient queue administration to limit quiet hold up deferrals and patient congestion is one of the significant difficulties looked by healing centers. Pointless and irritating sits tight for long stretches result in generous human asset and time wastage and increment the dissatisfaction persisted by patients.

For each patient in the line, the aggregate treatment time of the considerable number of patients before him is the time that he should wait. It would be helpful and best if the patients could get the most efficient treatment and know the anticipated holding up time through a versatile application that updates progressively. Hence, we propose a Patient Treatment Time Prediction (PTTP) calculation to foresee the sitting tight time for every treatment undertaking for a patient.

System Configuration:

H/W System Configuration:-

System          : Pentium I3 Processor.
Hard Disk       : 500 GB.
Monitor          : Standard LED Monitor
Input Devices : Keyboard
Ram               : 4 GB

S/W System Configuration:-

Operating system              : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database                          : MYSQL

Conclusion A Parallel Patient Treatment Time Prediction Algorithm and its Applications in Hospital Queuing-Recommendation in a Big Data Environment big data project report paper, a PTTP algorithm based on big data and the Apache Spark cloud environment is proposed. A random forest optimization algorithm is performed for the PTTP model. The queue waiting time of each treatment task is predicted based on the trained PTTP model. A parallel HQR system is developed, and an efficient and convenient treatment plan is recommended for each patient.

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