A Comparative Study of Machine Learning, Deep Learning Algorithms, and Explainable AI Techniques for Diabetes Prediction

Muhammad Imad; Muhammad Shakeel; Hamail Raza Zaidi; Zabih Ullah Khan

doi:10.4018/979-8-3373-0690-2.ch005

A Comparative Study of Machine Learning, Deep Learning Algorithms, and Explainable AI Techniques for Diabetes Prediction

Muhammad Imad
, Muhammad Shakeel
, Hamail Raza Zaidi
, Zabih Ullah Khan

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Diabetes prediction remains a crucial area of research due to its profound impact on global health. Diabetes, a chronic metabolic disorder, affects millions of people worldwide and poses significant challenges to healthcare systems. Early prediction and diagnosis are essential to managing the disease effectively, preventing complications, and improving the quality of life for patients. Recent advancements in artificial intelligence (AI) have paved the way for powerful tools in diabetes prediction, particularly through machine learning and deep learning algorithms. These methods offer promising solutions for enhancing early diagnosis and personalized care.

Original language	English
Title of host publication	Utilizing AI of Medical Things for Healthcare Security and Sustainability
Publisher	IGI Global
Pages	157-180
Number of pages	24
ISBN (Electronic)	9798337306926
ISBN (Print)	9798337306902, 9798337306919
DOIs	https://doi.org/10.4018/979-8-3373-0690-2.ch005
Publication status	Published (in print/issue) - 14 Feb 2025

Bibliographical note

Publisher Copyright:
© 2025, IGI Global Scientific Publishing.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.4018/979-8-3373-0690-2.ch005

Cite this

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A Comparative Study of Machine Learning, Deep Learning Algorithms, and Explainable AI Techniques for Diabetes Prediction. / Imad, Muhammad; Shakeel, Muhammad; Zaidi, Hamail Raza et al.
Utilizing AI of Medical Things for Healthcare Security and Sustainability. IGI Global, 2025. p. 157-180.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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PY - 2025/2/14

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AB - Diabetes prediction remains a crucial area of research due to its profound impact on global health. Diabetes, a chronic metabolic disorder, affects millions of people worldwide and poses significant challenges to healthcare systems. Early prediction and diagnosis are essential to managing the disease effectively, preventing complications, and improving the quality of life for patients. Recent advancements in artificial intelligence (AI) have paved the way for powerful tools in diabetes prediction, particularly through machine learning and deep learning algorithms. These methods offer promising solutions for enhancing early diagnosis and personalized care.

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ER -

A Comparative Study of Machine Learning, Deep Learning Algorithms, and Explainable AI Techniques for Diabetes Prediction

Abstract

Bibliographical note

UN SDGs

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