AI Techniques for Association Rule Mining in Medical Data: Trends and Practical Applications

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Anandhavalli Muniasamy

AI Techniques for Association Rule Mining in Medical Data

Name: AI Techniques for Association Rule Mining in Medical Data
Price: 450.14 AUD
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ISBN: 9798337366920

Trends and Practical Applications

Anandhavalli Muniasamy

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Paperback

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English

Medical Information Science Reference
25 February 2026

Health systems & services; Data capture & analysis; Artificial intelligence

The rapid growth of electronic health records and biomedical repositories has created unprecedented opportunities to extract patterns from medical data. Among the most effective approaches for uncovering hidden relationships is association rule mining, a data mining technique designed to identify patterns and correlations among variables. In recent years, advances in artificial intelligence (AI) have significantly enhanced interpretability when dealing with complex medical datasets. AI Techniques for Association Rule Mining in Medical Data: Trends and Practical Applications explores how AI is revolutionizing the process of discovering patterns and relationships in medical datasets. It examines how hybrid and intelligent models enable more accurate and clinically relevant pattern discovery in high-dimensional medical datasets. Covering topics such as AI, medical data, and medical technologies, this book is an excellent resource for academicians, data scientists, healthcare professionals, software engineers, AI developers, and graduate students.

Edited by: Anandhavalli Muniasamy
Imprint: Medical Information Science Reference
Country of Publication: United States
Dimensions: Height: 254mm, Width: 178mm,
ISBN: 9798337366920
Pages: 466
Publication Date: 25 February 2026
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

Dr. Anandhavalli Muniasamy is an Associate Professor in the Department of Informatics and Computer Systems, College of Computer Science at King Khalid University (KKU), Saudi Arabia. She holds a Ph.D. in Computer Science & Engineering from SMIT, Sikkim Manipal University, India. Her research expertise spans data mining, data analytics, artificial intelligence (AI), soft computing, and data science, with a particular focus on machine learning and deep learning applications in healthcare data analysis. She has an impressive academic and research portfolio, having published over 40 research articles in prestigious international journals, conferences, and book chapters. She has actively participated in more than 35 national and international conferences, presenting her work on cutting-edge computational techniques and contributing to edited book publications. A recognized innovator, she holds four patents, demonstrating her expertise in advanced computational methodologies. As a Principal Investigator (PI) and Co-Principal Investigator (Co-PI), she has led five university-funded research projects and served as PI for a funded project supported by the All India Council for Technical Education (AICTE). Beyond her research, she has been a dedicated manuscript reviewer for leading international journals for over a decade and a Ph.D. thesis examiner for multiple universities. She is frequently invited to deliver guest lectures and serves as an editorial board member for various renowned journals and conferences. She is an active member of professional organizations such as IEEE, the International Association of Engineers (IAENG), the Computer Society of India (CSI), and the International Association of Computer Science and Information Technology (IACSIT). Her current research focuses on leveraging AI-driven techniques in healthcare analytics, particularly machine learning and deep learning, to address complex challenges in medical data analysis and drive innovative solutions in her field.