Abstract:
A chronic illness that affects millions of individuals worldwide, diabetes poses serious health
hazards if left untreated. Improving patient outcomes and limiting serious consequences can be
achieved by accurately predicting diabetes and detecting it early. This research aims to create a
diabetes prediction model by applying the Support Vector Machine (SVM) algorithm, a potent
supervised machine learning method well-known for its efficiency in classification applications.
A dataset with numerous health-related characteristics, including blood pressure, body mass index
(BMI), age, and glucose levels, was used to train and assess the model. Making a trustworthy and
accurate algorithm that could identify people as either diabetics or non-diabetics based on their
data was the main goal.
The model was created using Python and other libraries, including Scikit-learn for machine
learning tasks, Pandas and NumPy for data processing, and Matplotlib and Seaborn for data
visualization. In addition, a Django web application with HTML, CSS, Bootstrap, and JavaScript
was created to offer an interactive user interface for data entry and prediction viewing. The
backend database utilized to store user data and prediction history was SQLite. The outcomes
showed that the SVM model had a good accuracy rate, which made it a useful tool for clinical
diabetes prediction. This paper offers a useful implementation methodology for incorporating
predictive models into user-friendly online apps and highlights the possibilities of machine
learning in the healthcare industry.
To further enhance the accuracy and generalizability of the diabetes prediction model, future
research should consider integrating additional features such as genetic data and lifestyle factors,
as well as exploring more complex algorithms like deep learning models. Implementing the system
in a real clinical setting could also help validate its effectiveness in practice. Additionally,
expanding the scope of the study to include different population demographics could ensure that
the model is applicable across diverse patient groups. Regular updates and improvements based
on user feedback and advances in machine learning techniques are essential to maintaining the
system's relevance and effectiveness in predicting diabetes.
