• V. Varun Sai
• V.Ashwin Reddy
• T.Satwika
• B.Sujani Reddy

Diabetes, a persistent ailment with the potential to precipitate a global healthcare crisis, affects a substantial portion of the global populace. According to the International Diabetes Federation, a staggering 382 million individuals currently grapple with diabetes worldwide. The underlying pathology of diabetes lies in the escalation of blood glucose levels, resulting in distressing symptoms like frequent urination, and insatiable hunger. Tragically, diabetes serves as the principal catalyst for an array of grave health complications, such as renal failure, heart failure, blindness, and so on, and strokes. The malady manifests in diverse forms, with diabetes type 1 and type 2 being the most prevalent, while gestational diabetes emerges during pregnancy, among other variations. This entails amalgamating the outcomes including several machine learning methods, such as Random Forest, Support Vector Machine, Logistic Regression, and Decision Tree algorithms.

Diabetes,bloodglucose,pregnancy, Randomforest, Logistic Regression

"Diabetes Detection Using Logistic regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d607-d611, April-2024, Available :http://www.jetir.org/papers/JETIR2404377.pdf

"Diabetes Detection Using Logistic regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd607-d611, April-2024, Available at : http://www.jetir.org/papers/JETIR2404377.pdf