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Synopsis
Integrative approaches that combine machine learning (ML) and optimization algorithms rapidly transform the landscape of disease prediction and healthcare analytics. By leveraging the predictive power of ML models alongside the efficiency of optimization techniques, researchers can develop more accurate, robust, and scalable systems for early diagnosis and risk assessment. These hybrid frameworks enable the integration of diverse data sources into cohesive predictive models. The synergy between ML and optimization enhances model performance while supporting personalized medicine by tailoring predictions to individual patient profiles. Integrative methodologies hold significant promises for advancing clinical decision-making and improving health outcomes. Integrative Machine Learning and Optimization Algorithms for Disease Prediction explores the cutting-edge applications of machine learning, deep learning, and optimization algorithms in disease prediction. It examines how diverse machine learning models, from traditional algorithms to deep learning and ensemble methods, can be optimized for high-stakes clinical predictions. This book covers topics such as disease prediction, healthcare data, and mental health, and is a useful resource for computer engineers, medical professionals, academicians, researchers, and scientists.
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About the Author
Dr. Anandhavalli Muniasamy is an Associate Professor in the Department of Computer Science at King Khalid University (KKU), Saudi Arabia. She holds a Ph.D. in Computer Science and Engineering from Sikkim Manipal University, India. Her research interests span data mining, data analytics, artificial intelligence (AI), soft computing, and data science, with a focus on machine learning and deep learning for advanced data analysis and medical data analysis. She has published over 35 articles in international journals and book chapters and presented at more than 30 national and international conferences. Dr. Muniasamy has led several university-funded projects and served as Principal Investigator for an AICTE-funded project. She is a reviewer for reputed journals, a Ph.D. thesis examiner, and an editorial board member. She actively contributes to organizations such as IAENG, CSI, and IACSIT.
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Taschenbuch. Condition: Neu. Integrative Machine Learning and Optimization Algorithms for Disease Prediction | Anandhavalli Muniasamy | Taschenbuch | Englisch | 2025 | IGI Global | EAN 9798337310886 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133652134
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Integrative approaches that combine machine learning (ML) and optimization algorithms rapidly transform the landscape of disease prediction and healthcare analytics. By leveraging the predictive power of ML models alongside the efficiency of optimization techniques, researchers can develop more accurate, robust, and scalable systems for early diagnosis and risk assessment. These hybrid frameworks enable the integration of diverse data sources into cohesive predictive models. The synergy between ML and optimization enhances model performance while supporting personalized medicine by tailoring predictions to individual patient profiles. Integrative methodologies hold significant promises for advancing clinical decision-making and improving health outcomes. Integrative Machine Learning and Optimization Algorithms for Disease Prediction explores the cutting-edge applications of machine learning, deep learning, and optimization algorithms in disease prediction. It examines how diverse machine learning models, from traditional algorithms to deep learning and ensemble methods, can be optimized for high-stakes clinical predictions. This book covers topics such as disease prediction, healthcare data, and mental health, and is a useful resource for computer engineers, medical professionals, academicians, researchers, and scientists. Seller Inventory # 9798337310886