DIABETICS DETECTION USING PYTHON

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Sai Priya R; Sarin Priya D

DIABETICS DETECTION USING PYTHON

Authors: Sai Priya R, Sarin Priya D

Unique Paper ID: 175929
Volume: 11
Issue: 11
PageNo: 4367-4372

Keywords: No Keywords Found

Abstract:
Diabetes is a significant global health issue that necessitates early diagnosis and management to prevent severe complications. The project aims to develop a web based application using Flask and machine learning to predict the likelihood of diabetes. Users input specific health parameters such as age, BMI, glucose levels, and insulin levels through a user-friendly web interface. The backend, powered by Flask, processes these inputs using a pre-trained machine learning model (e.g., Random Forest Classifier) to provide instant predictions. The application includes robust security measures to ensure the privacy and confidentiality of user data. Additionally, it logs user inputs and prediction results to continuously improve the model's accuracy. The system architecture involves a responsive front-end, a Flask based backend, and a secure database for storing user data. This approach facilitates early diagnosis and timely management, potentially improving health outcomes and providing a valuable tool for both individuals and healthcare providers.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4367-4372

DIABETICS DETECTION USING PYTHON

Available:https://ijirt.org/article?manuscript=175929

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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