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Early-Stage Dementia Detection by Optimize Feature Weights with Ensemble Learning

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Advanced Communication and Intelligent Systems (ICACIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1749))

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Abstract

Dementia has become a serious health concern for many people above fifty years. Several types of dementia typically applied and in stages. Past studies have received a report from persons of different ages who have afflicted from long-term memory problems and constantly reflecting because of neuro-degenerative illness. Dementia is defined by irreversible and serious memory loss. Though it is more prevalent in the elder people, an increase in cases among the younger age group has stimulated professionals' interest and inspired them to examine the nerve cells, which an lead to memory lapses and difficulty remembering information stored in memory. Dementia can often be decreased to some extent if identified early enough. Additional tree classifier and Optimize learning are used to extract information from MRI Brain images and characterise dementia at initial point. The hyper-parameters achieved from XGboost were determined in order to explore different forms of mortality risk. Gradient boosting is a method that is frequently used to derive variables from independent to dependent variables, in addition to the derived variables which outcome from this process.

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Authors and Affiliations

  1. National Institute of Technology, Hamirpur, Himachal Pradesh, India

    Tanvi Mahajan & Jyoti Srivastava

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  1. Tanvi Mahajan
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  2. Jyoti Srivastava
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Correspondence to Tanvi Mahajan .

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Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, India

    Rabindra Nath Shaw

  2. Systems Research Institute, Warsaw, Poland

    Marcin Paprzycki

  3. The Neotia University, Sarisha, India

    Ankush Ghosh

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Mahajan, T., Srivastava, J. (2023). Early-Stage Dementia Detection by Optimize Feature Weights with Ensemble Learning. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_56

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  • DOI: https://doi.org/10.1007/978-3-031-25088-0_56

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