Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data

Authors

  • Aunsia Khan Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad.
  • Muhammad Usman Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad.

DOI:

https://doi.org/10.3991/ijoe.v15i15.11472

Keywords:

Alzheimer’s disease prediction, Naïve Bayes Classifier, Class imbalance, Machine learning, early diagnosis

Abstract


Diagnosing Alzheimer’s disease (AD) is usually difficult, especially when the disease is in its early stage. However, treatment is most likely to be effective at this stage; improving the diagnosis process. Several AD prediction models have been proposed in the past; however, these models endure a number of limitations such as small dataset, class imbalance, feature selection methods etc which place strong barriers towards the accurate prediction. In this paper, an AD prediction model has been proposed and validated using categorical dataset from National Alzheimer’s Coordination Center (NACC). The different categories such as Demographics, Clinical Diagnosis, MMSE & Neuropsychological battery, is preprocessed for important features selection and class imbalance. A number of predominant classifiers namely, Naïve Bayes, J48, Decision Stump, LogitBoost, AdaBoost, and SDG-Text have been used to highlight the superiority of a classifier in predicting the potential AD patients. Experimental results revealed that Bayesian based classifiers improve AD detection accuracy up to 96.4% while using Clinical Diagnosis category.

Author Biographies

Aunsia Khan, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad.

Aunsia Khan is PhD Student at Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad. She has published various papers in Conference proceedings as well as in IF journals. (Email: aunsia.khan@szabist-isb.edu.pk)

Muhammad Usman, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad.

Dr. Muhammad Usman has completed PhD in Computer & Information Sciences from Auckland University of Technology, New Zealand. He is currently an Associate Professor of Computer Science in the department of Computing at Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan. His research interests include Data Mining, Data Warehousing, OLAP, Business Intelligence and Knowledge discovery. He is currently researching in the novel methods and techniques for the seamless integration of Data Mining and Data Warehousing technologies. 

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Published

2019-12-17

How to Cite

Khan, A., & Usman, M. (2019). Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data. International Journal of Online and Biomedical Engineering (iJOE), 15(15), pp. 96–109. https://doi.org/10.3991/ijoe.v15i15.11472

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Papers