A Web Application for Predicting Diabetes Using Machine Learning Methods

Year : 2024 | Volume : 11 | Issue : 03 | Page : 92 102
    By

    Riyaz Ahmed,

  • Piyush Kumar Gupta,

  1. Student, Department of Computer Science and Engineering, School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India
  2. Assistant Professor, Department of Computer Science and Engineering, School of Engineering Science and Technology, Jamia Hamdard University, New Delhi, India

Abstract

Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. The various methodologies to be applied in diverse models entail K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), support vector machine (SVM), (RF Computers can predict if someone has diabetes by recognizing signs. We will integrate the trained models into a web application that will connect the models to provide real-time predictions based on factors responsible for diabetes such as body mass index (BMI), age, and insulin levels. We utilized the Kaggle dataset to construct a machine learning-based model for predicting diabetes. We developed a Flask application for diabetes prediction to provide insights into health status and risk factors. We organized the app into modules, such as routes, templates, forms, and static assets, and used Flask’s modular structure. Our site uses HTML, CSS, and JavaScript to create dynamic content and enable interactivity. Machine learning generates a diabetes risk score based on personal details. We have deployed this algorithm to the web with Flask (a Python framework). Predictive models in healthcare could lead to better patient outcomes. Health providers might employ them through predictive analytics within electronic medical records, mobile apps for monitoring individuals’ wellness statuses, or population health management systems that aim at identifying those most in need so that interventions can be prioritized accordingly while keeping track over time. It would be essential for future research efforts towards the integration of various data sources if we are to obtain more accurate results.

Keywords: Machine learning, diabetes prediction, application technology, health, SVM model

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Riyaz Ahmed, Piyush Kumar Gupta. A Web Application for Predicting Diabetes Using Machine Learning Methods. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):92-102.
How to cite this URL:
Riyaz Ahmed, Piyush Kumar Gupta. A Web Application for Predicting Diabetes Using Machine Learning Methods. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):92-102. Available from: https://journals.stmjournals.com/joaira/article=2024/view=177243


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 18/05/2024
Accepted 26/09/2024
Published 07/10/2024
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