In recent years, the prevalence of diabetes has emerged as a critical public health concern, emphasizing the need for efficient diagnostic tools that enable early detection and effective management. This research paper presents a robust machine learning framework designed to predict and classify diabetes by leveraging patient specific clinical data. Our approach utilizes a combination of supervised learning algorithms, including logistic regression and random forests, and support vector machines, to accurately diagnose diabetes and categorize patients based on disease progression. By optimizing feature selection and algorithmic performance, we demonstrate improved accuracy, sensitivity and specificity over traditional diagnostic methods. The model’s predictive capability offers significant potential in identifying at risk individuals, allowing for timely interventions and personalized treatment strategies. This work highlights the transformative role of machine learning in enhancing diagnostic precision, facilitating a paradigm shift in diabetes management and patient care.
Diabetes Prediction, Machine Learning, Diabetes Classification, Supervised Learning, Clinical Data, Early Diagnosis, Disease Progression, Logistic Regression, Feature Selection, Predictive Modeling, Healthcare Analytics, Diagnostic Accuracy.
International Journal of Trend in Scientific Research and Development - IJTSRD having
online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International
Journal which provides rapid publication of your research articles and aims to promote
the theory and practice along with knowledge sharing between researchers, developers,
engineers, students, and practitioners working in and around the world in many areas
like Sciences, Technology, Innovation, Engineering, Agriculture, Management and
many more and it is recommended by all Universities, review articles and short communications
in all subjects. IJTSRD running an International Journal who are proving quality
publication of peer reviewed and refereed international journals from diverse fields
that emphasizes new research, development and their applications. IJTSRD provides
an online access to exchange your research work, technical notes & surveying results
among professionals throughout the world in e-journals. IJTSRD is a fastest growing
and dynamic professional organization. The aim of this organization is to provide
access not only to world class research resources, but through its professionals
aim to bring in a significant transformation in the real of open access journals
and online publishing.