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A Novel Hybrid Machine Learning Approach Using Deep Learning for the Prediction of Alzheimer Disease Using Genome Data

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

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Abstract

Genome-wide association studies are aimed at identifying associations between commonly occurring variations in a group of individuals and a phonotype, in which the Deoxyribonucleic acid is genotyped in the form of single nucleotide polymorphisms. Despite the exsistence of various research studies for the prediction of chronic diseases using human genome data, more investigations are still required. Machine learning algorithms are widely used for prediction and genome-wide association studies. In this research, Random Forest was utilised for selecting most significant single nucleotide polymorphisms associated to Alzheimer’s Disease. Deep learning model for the prediction of the disease was then developed. Our extesnive similation results indicated that this hybrid model is promising in predicting individuals that suffer from Alzheimer’s disease, achieving area under the curve of 0.9 and 0.93 using Convolutional Neural Network and Multilayer perceptron respectively.

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Authors and Affiliations

  1. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    A. Alatrany, A. Hussain & D. Al-Jumeily

  2. University of Information Technology and Communications, Baghdad, Iraq

    A. Alatrany

  3. Kazan Federal University, Kazan, Russia

    J. Mustafina

Authors
  1. A. Alatrany
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  2. A. Hussain
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  3. J. Mustafina
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  4. D. Al-Jumeily
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Corresponding author

Correspondence to A. Alatrany .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Alatrany, A., Hussain, A., Mustafina, J., Al-Jumeily, D. (2021). A Novel Hybrid Machine Learning Approach Using Deep Learning for the Prediction of Alzheimer Disease Using Genome Data. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_23

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  • DOI: https://doi.org/10.1007/978-3-030-84532-2_23

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