Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques

Authors

  • Rishita Konda Vellore Institute of Technology University image/svg+xml
  • Anuraag Ramineni Vellore Institute of Technology University image/svg+xml
  • Jayashree J Vellore Institute of Technology University image/svg+xml
  • Niharika Singavajhala Vasavi College of Engineering
  • Sai Akshaj Vanka Vasavi College of Engineering

DOI:

https://doi.org/10.4108/eetpht.10.5497

Keywords:

Mellitus, Embedded Technique, Machine Learning, SGN Algorithm

Abstract

 

INTRODUCTION: The goal of this study, titled ”Integrated System for Detecting Diabetes Mellitus using Various Machine Learning and Deep Learning Algorithms,” is to increase the precision and usability of diabetes diagnosis through the investigation and application of a wide range of machine learning and deep learning techniques.

OBJECTIVES: The objective of the study was to establish a comprehensive system for identifying diabetes mellitus by combining several machine learning and deep learning methods

METHODS: The methodology included every phase, from data gathering and preprocessing through advanced model development and performance assessment. The experiment demonstrated how combining several machine learning and deep learning techniques might completely transform diabetes detection. While praising accomplishments, the methodology also highlighted flaws in the data collection process. The goal of the roadmap for future improvements was to use technology to better detect and treat diabetes, which would ultimately help people of all ages and backgrounds.

RESULTS: The project’s remarkable results demonstrate the legitimacy of the methodology chosen while also highlighting its potential to completely transform the diagnosis and treatment of diabetes

CONCLUSION: The conclusion of this project lays the ground for next developments, such as improved user interfaces and the expansion of dataset scope. Through these initiatives, the long-term objective of providing more precise and accessible diabetes diagnoses becomes a real possibility, providing significant advantages to people from a variety of age groups and demographics[6].

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Published

21-03-2024

How to Cite

1.
Konda R, Ramineni A, J J, Singavajhala N, Vanka SA. Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 21 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5497