Revolutionizing Diabetes Diagnosis: The Power of Machine Learning and Deep Learning

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Gananjay Sandeep Thanekar

Revolutionizing Diabetes Diagnosis: The Power of Machine Learning and Deep Learning

Authors: Gananjay Sandeep Thanekar

Unique Paper ID: 182859
Volume: 12
Issue: 2
PageNo: 3407-3410

Keywords: Diabetes Diagnosis Machine Learning Deep Learning Artificial Intelligence Support Vector Machines (SVM) Convolutional Neural Networks (CNN)

Abstract:
Diabetes is a chronic condition affecting millions worldwide, making early and accurate diagnosis essential for effective management. Traditional diagnostic methods are often time-consuming and susceptible to human error. However, advancements in machine learning (ML) and deep learning (DL) have revolutionized disease detection, enabling automated, efficient, and precise diabetes diagnosis. This paper explores various ML and DL models, including decision trees, support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for diabetes prediction. It examines key datasets, feature selection techniques, and performance metrics to evaluate model effectiveness. Additionally, the study discusses the benefits and challenges of AI-driven diagnostic tools, emphasizing the need for robust, interpretable, and clinically validated models for real-world healthcare applications.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 2
PageNo: 3407-3410

Revolutionizing Diabetes Diagnosis: The Power of Machine Learning and Deep Learning

Available:https://ijirt.org/article?manuscript=182859

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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