Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Integrative Machine Learning and Optimization Algorithms for Disease Prediction

Integrative Machine Learning and Optimization Algorithms for Disease Prediction

Muniasamy, Anandhavalli

Paperback

Medical ReferenceGeneral ComputersProgramming

Publisher Price: $210.00

ISBN13: 9798337310886
Publisher: IGI Global
Published: Jul 3 2025
Pages: 434
Weight: 1.65
Height: 0.88 Width: 7.00 Depth: 10.00
Language: English
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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