Early Alzheimer’s Disease Prediction in Machine Learning Setup: Empirical Analysis with Missing Value Computation

  • Sidra MinhasEmail author
  • Aasia Khanum
  • Farhan Riaz
  • Atif Alvi
  • Shoab A. Khan
  • Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)


Alzheimer’s Disease (AD) is the most prevalent progressive neurodegenerative disorder of the elderly. Prospective treatments for slowing down or pausing the process of AD require identification of the disease at an early stage. Many patients with mild cognitive impairment (MCI) may eventually develop AD. In this study, we evaluate the significance of using longitudinal data for efficiently predicting MCI-to-AD conversion a few years ahead of clinical diagnosis. The use of longitudinal data is generally restricted due to missing feature readings. We implement five different techniques to compute missing feature values of neuropsychological predictors of AD. We use two different summary measures to represent the artificially completed longitudinal features. In a comparison with other recent techniques, our work presents an improved accuracy of 71.16 % in predicting pre-clinical AD. These results prove feasibility of building AD staging and prognostic systems using longitudinal data despite the presence of missing values.


Machine learning Alzheimer’s Disease (AD) Mild Cognitive Impairment (MCI) ADNI Longitudinal data Missing value Support vector machine Classification AUC 



We would like to thank all investigators of ADNI listed at:, for developing and making their data publically available.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sidra Minhas
    • 1
    Email author
  • Aasia Khanum
    • 2
  • Farhan Riaz
    • 1
  • Atif Alvi
    • 2
  • Shoab A. Khan
    • 1
  • Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Computer Engineering Department, College of E&MENational University of Science and Technology (NUST)IslamabadPakistan
  2. 2.Computer Science DepartmentForman Christian CollegeLahorePakistan

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