Year: 2025
Type: article
Abstract: Diabetes mellitus is one of the most pressing global health issues, affecting millions worldwide. Early prediction and timely management can significantly reduce the disease's impact and improve the quality of life for individuals at risk. This research presents a detailed and human- centric approach to building a diabetes prediction model using machine learning algorithms. By leveraging real-world patient data, we explore various supervised learning techniques, assess their accuracy, and highlight the importance of interpretability in predictive healthcare. This paper emphasizes the ethical implications, real-world applications, and the need to bridge the gap between technology and patient-centered care.
Authors: Mohammad Fahad, Majid Hussain, Mohd Arif Khan, Ehteshaam Hussain
Institution: Integral University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4409830955