Year: 2025
Type: article
Abstract: This article explores the use of Support Vector Machines (SVM) for diagnosing diabetes based on fourteen medical and behavioral variables. Following a theoretical overview of diabetes and SVM, a Python implementation is presented, including visualization of the hyperplane and margins through dimensionality reduction (PCA). The model stands out by training on the full set of variables without prior feature selection, ensuring complete exploitation of the available information. A practical case involving the insertion of a new patient is also addressed, illustrating the real-world application of the model. The achieved performance (accuracy, precision, recall) is evaluated and compared to that of other machine learning approaches, such as neural networks using the same dataset. The study concludes with a discussion on the results and perspectives for computer-assisted medical diagnosis.
Source: Open Journal of Applied Sciences
Authors: Aya Patricia Konan, Adama Coulibaly, Kouassi Bernard Saha, Souleymane Oumtanaga
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411708282