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Alzheimer Disease Prediction Using Learning Algorithms

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Applied Computational Technologies (ICCET 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 303))

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Abstract

In the current scenario, the health sector needs practical actions for improvement. Alzheimer’s is one of the severe neurodegenerative diseases. Its detection is essential at the beginning or early stage to get better treatment. A more effective result can be gained if disease prediction can be made at the initial level. It is vital and tedious to diagnose this disease. Machine Learning (ML) is showing compelling results in every sphere. Nowadays, it has become a booming area due to remarkable achievements gained in decision making. The paramount importance of the health sector attracts this research for applying machine learning paradigms to contribute to the betterment of disease diagnosis. Voluminous data generation in the health area is also why using ML for detection. This study elaborates on ML algorithms in detail and in a comparative fashion. This letter describes ML approaches like Logistic regression (LR), i.e., a binary classifier, Randon Forest (RF), which is a combination of Decision Tree (DT), boosting Adaboost, and Support Vector Machine (SVM).

Further, this document applies ML models on the MRI dataset called oasis_longitudinal. The results obtained through the experiments are presented in three terms called AUC, Recall, and Accuracy. This article compares the efficiency of mentioned algorithms in tabular and graphical forms. RF (with 0.8421 accuracies) shows better results than others, and DT (with 0.068788 s.) takes minimum time to accomplish the required classification task. Thus, this study contributes to knowing the efficiency of ML paradigms in detecting Alzheimer’s disease. Here, five different ML classifiers are applied to the MRI dataset, and their results are compared.

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Authors and Affiliations

  1. Banasthali Vidyapith, Jaipur, 302001, Rajasthan, India

    Vidushi & Manisha Agarwal

  2. KIET Group of Institutions, Delhi-NCR, Ghaziabad, 201206, India

    Vidushi, Akash Rajak & Ajay Kumar Shrivastava

Authors
  1. Vidushi
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  2. Manisha Agarwal
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  3. Akash Rajak
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  4. Ajay Kumar Shrivastava
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Corresponding author

Correspondence to Vidushi .

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Editors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Dr. B. A. Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  2. Department of Digital Education and Policy, Swansea University, Swansea, UK

    Tom Crick

  3. Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

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Vidushi, Agarwal, M., Rajak, A., Shrivastava, A.K. (2022). Alzheimer Disease Prediction Using Learning Algorithms. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_31

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