Machine Learning Based Diabetes Prediction System: A Novel Approach

Authors

  • Shalini Shekhar Research Scholar, Department of Computer Science, Sai Nath University, Ranchi, Jharkhand, India Author
  • Nikita Thakur Associate Professor, Department of Computer Science, Sai Nath University, Ranchi, Jharkhand, India Author

Keywords:

Diabetes Prediction System, Machine Learning, Classification Models

Abstract

The healthcare sector is poised to  experience a remarkable transformation with the  integration of artificial intelligence. In the realm of  healthcare analysis and prediction, the utilization of data  science and machine learning applications proves  advantageous. Healthcare is emerging as a progressive and  promising field for the implementation of data science  applications, particularly in Medical Images Analysis,  Drug Discovery, Genetics Research, and Predictive  Medicine. Diabetes is broadly classified into three main  types: type 1, type 2, and gestational diabetes. The primary  objective of this research is to develop a Machine Learning  Model for the diagnosis of diabetes. Identifying the  accurate symptoms in users or individuals with diabetes is  a significant challenge for application and the execution of  rules. These combinations of knowledge determine  whether an individual is a diabetes patient, including its  subtypes such as type_1, type_2, and gestational diabetes.  The Machine Learning Model underwent testing on a  cohort of 150 patients, producing results comparable to  those of medical professionals. 

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Published

2023-12-30

How to Cite

Machine Learning Based Diabetes Prediction System: A Novel Approach. (2023). International Journal of Innovative Research in Engineering & Management, 10(6), 92–99. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/12982