This paper is published in Volume-11, Issue-1, 2025
Area
Diabetes Prognosis
Author
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh
Org/Univ
Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India
Keywords
Diabetes in Pregnant Women, Machine Algorithms
Citations
IEEE
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh. Diabetes Prognosis Using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh (2025). Diabetes Prognosis Using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 11(1) www.IJARIIT.com.
MLA
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh. "Diabetes Prognosis Using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 11.1 (2025). www.IJARIIT.com.
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh. Diabetes Prognosis Using Machine Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh (2025). Diabetes Prognosis Using Machine Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 11(1) www.IJARIIT.com.
MLA
Anshika Sharma, Divasha alag, Atharva, Aditya Pratap Singh. "Diabetes Prognosis Using Machine Learning." International Journal of Advance Research, Ideas and Innovations in Technology 11.1 (2025). www.IJARIIT.com.
Abstract
Diabetes is a prolonged disorder brought on by above-normal blood glucose levels, leading to symptoms like frequent urination, thirst, and hunger. It can May cause significant complications, such as blindness, kidney failure, heart failure, and stroke. The pancreas usually produces insulin to help cells absorb glucose for energy, but this process fails in diabetes. Machine learning offers tools for early diabetes prediction. Various algorithms, such as KNearest Neighbors, Logistic Regression, Random Forest, and Decision Tree, are evaluated to select the most accurate model for diagnosis.