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Classification and Feature Selection of Alzheimer’s Disease for MRI Data Utilizing Convolutional Neural Network and Support Vector Machine

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Abstract

Alzheimer's disease (AD) is a neurological disease that affect numerous people. According to the literature, forecasting this type of disease can be an extremely difficult process. Machine Learning and Artificial Intelligence play a significant part in healthcare and other relevant fields. Deep Learning prediction model can be used by taking into consideration the basic principles of Machine Learning and Deep Learning algorithm. There are wide range of feature selection and medical image preprocessing approaches, and applied multiple classification algorithms to the datasets to determine which methods produced the best results. This paper proposes by considering Convolutional Neural network and Support vector machine as a base for classification and evaluated the model using a variety of methodologies to obtain the best possible outcome. That is considerably more effective than prior work in the same field.

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Data Availability

Upon reasonable request, the dataset utilized and examined in this study can be acquired from the corresponding author.

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Acknowledgements

The authors acknowledged the St Aloysius College (Autonomous), Mangaluru, India and Srinivas Institute of Technology, Mangaluru, India. for supporting the research work by providing the facilities.

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Authors and Affiliations

  1. Department of Computer Science and Application, St Aloysius College (Autonomous), Mangaluru, India

    Archana Yashodhar

  2. Srinivas Institute of Technology, Mangaluru, India

    Shashidhar Kini

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  1. Archana Yashodhar
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  2. Shashidhar Kini
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The success of this research endeavour is attributed to the collaborative efforts and valuable contributions of all authors involved.

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Correspondence to Archana Yashodhar.

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Yashodhar, A., Kini, S. Classification and Feature Selection of Alzheimer’s Disease for MRI Data Utilizing Convolutional Neural Network and Support Vector Machine. SN COMPUT. SCI. 5, 707 (2024). https://doi.org/10.1007/s42979-024-03019-5

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