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Handbook of Large-Scale Distributed Computing in Smart Healthcare pp 157–176Cite as

Large Scale Medical Data Mining for Accurate Diagnosis: A Blueprint

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Part of the book series: Scalable Computing and Communications ((SCC))

Abstract

Medical care and machine learning are associated together in the current era. For example, machine learning (ML) techniques support the medical diagnosis process/decision making on large scale of diseases. Advanced data mining techniques in diseases information processing context become essential. The present study covered several aspects of large scale knowledge mining for medical and diseases investigation. A genome-wide association study was reported including the interactions and relationships for the Alzheimer disease (AD). In addition, bioinformatics pipeline techniques were implied for matching genetic variations. Moreover, a novel ML approaches to construct a framework for large scale gene-gene interactions were addressed. Particle swam optimization (PSO) based cancer cytology is another discussed pivotal field. An assembly ML Random forest algorithm was mentioned as it was carried out to classify the features that are responsible for Bacterial vaginosis (BV) in vagina microbiome. Karhunen-Loeve transformation assures features finding from various level of ChIP-seq genome dataset. In the current work, some significant comparisons were conducted based on several ML techniques used for diagnosis medical datasets.

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Authors and Affiliations

  1. Computer Science and Engineering, East West University Bangladesh, Dhaka, Bangladesh

    Md. Sarwar Kamal

  2. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  3. Faculty of Engineering, Department of Electronics and Electrical Communications Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

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  1. Md. Sarwar Kamal
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  2. Nilanjan Dey
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Correspondence to Nilanjan Dey .

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  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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© 2017 Springer International Publishing AG

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Sarwar Kamal, M., Dey, N., Ashour, A.S. (2017). Large Scale Medical Data Mining for Accurate Diagnosis: A Blueprint. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-58280-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58279-5

  • Online ISBN: 978-3-319-58280-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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