Urban Planning Based Sustainable Public Healthcare System using Machine Learning Algorithms

 

 

 

V. Rajathi1, Pritee Parwekar2, V. Anantha Lakshmi3, M. Syed Rabiya4, M. Banu Priya5 V. Devi6

 

1Department of Computing Technologies, School of Computing, SRM institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India

 

2Professor, Department of CSE, GITAM School of Technology, GITAM University, Hyderabad, India

 

3Assistant Professor, Department of CSE (AI&ML), Pragati Engineering College, Surampalem Andhra Pradesh, India

 

4Assistant Professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

 

5Assistant Professor, Department of Computer Science and Engineering (Cyber security) Karpagam college of Engineering, Coimbatore, India

 

6Professor, PG Department of Computer Science, Thiruthangal Nadar College, Selavayal, Chennai-51, India

 

Text Box: Abstract Growing use of a wide range of Internet of Medical Things (IoMT) devices and apps makes smart health an increasingly vulnerable area. One popular method for creating smart city solutions that benefit vital infrastructures over time, such smart healthcare, is IoMT. Because Bluetooth technology is flexible and uses few resources, it is used for short-range communication by many IoMT devices in smart cities. This research proposes novel technique in urban planning in smart public healthcare system utilizing ML algorithms. The smart healthcare system is developed based on secure honeynet cloud IoT model. Here the input smart healthcare-based health monitoring data is collected and processed for missing value removal and noise removal. Then this data classified and optimized using recurrent Bi-LSTM temporal Gaussian model with whale swarm particle colony optimization. Experimental analysis is carried out in terms of detection accuracy, precision, data integrity, throughput, recall, latency. proposed technique obtained 96% of Detection accuracy, 97% of Precision, 95% of Throughput, 88% of RECALL, 94% of LATENCY. Emails: ajathiv@srmist.edu.in; pparweka@gitam.edu; ananthalakshmi.v@pragati.ac.in; drsyedrabiyam@veltech.edu.in; mbanu26@gmail.com; vdevi78@yahoo.com

 

Received: March 02, 2025 Revised: June 06, 2025 Accepted: July 07, 2025

 

Keywords: Urban planning; Smart public healthcare; Machine learning algorithms; Health monitoring; Gaussian model