IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation

Author:

Swaminathan B.1,Choubey Siddhartha2,Anushkannan N. K3,Arumugam Jeevanantham4,Suriyakrishnaan K5,Almoallim Hesham S.6,Alharbi Sulaiman Ali7,Soma S. R.8,Mosissa Ramata9ORCID

Affiliation:

1. Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, Tamil Nadu 602105, India

2. Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, Durg, Chhattisgarh 491001, India

3. Department of Electronics and Communication Engineering, Kathir College of Engineering, Coimbatore, Tamil Nadu 641062, India

4. Department of Information Technology, Kongu Engineering College, Perundurai, Tamil Nadu 638060, India

5. Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu 636005, India

6. Department of Oral and Maxillofacial Surgery, College of Dentistry, King Saud University, PO Box 60169, Riyadh 11545, Saudi Arabia

7. Department of Botany and Microbiology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia

8. Department of Sciences, University of Tennessee Health Science Center, Memphis, USA

9. Department of IT, Mettu University, Metu, Ethiopia

Abstract

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.

Funder

King Saud University

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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