Diabetes Prediction and Apprehension with Focus Both on Clinical and Non-Clinical Factors

Authors

  • Aditya Gupta Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Angad Singh Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Maharshi Jani Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Yuvraj Salaria Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Vani Hiremani Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Sudhanshu Gonge Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India
  • Ketan Kotecha Department of Computer Science and engineering, Symbiosis Institute of Technology, Symbiosis International University (Deemed University) Pune, India

Keywords:

Classification, Regression, Probability, Machine Learning, Statistics

Abstract

An industrial revolution has changed the daily lifestyle of engineers, doctors and common people. This is due to a lot of experimental work performed and carried out in the food industry, IT sector, agriculture industry, automobile industry, etc. It has an impact on the diet of the stakeholders. Due to this the enzyme generation gets reduced and may lead to low production of insulin. As insulin is one of the important parts of the blood which controls all properties of plasma, water, enzymes, protein, vitamins and minerals, this causes diabetes to the patient. It is the essentially the most common and widespread chronic disease in the world. In our research paper both clinical and non-clinical parameters are considered such as Insulin, Glucose, BMI, smoking, stress, BP, Junk food etc. From this the aim of the research is to emphasize on both kinds of factors that affect a person’s probability of Diabetes detection. There are several techniques such as models of SVM, Decision Tree, Random Forest and Logistic Regression which were found useful for predicting and apprehending the features to identify diabetes. We have done comparative analysis of techniques to observe the output after applying clinical and non-clinical factors.

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References

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Published

25.12.2023

How to Cite

Gupta, A. ., Singh, A. ., Jani, M. ., Salaria, Y. ., Hiremani, V. ., Gonge, S. ., & Kotecha, K. . (2023). Diabetes Prediction and Apprehension with Focus Both on Clinical and Non-Clinical Factors. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 746–755. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4172

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

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