Predicting Alzheimer's Progression in Mild Cognitive Impairment: Longitudinal MRI with HMMs and SVM Classifiers

The number of elderly people has increased due to the huge growth in human life expectancy over the past few decades. As a result, age-related illnesses and ailments have become more prevalent, including Alzheimer's Disease (AD). A notable deterioration in cognitive functions, particularly memory and thinking skills, characterizes Mild Cognitive Impairment (MCI), a condition that lies in the middle of normal aging and dementia. Therefore, MCI carries a noticeably higher chance of developing into AD and frequently serves as a prelude to dementia. However, using cutting-edge image processing and machine learning techniques, it is possible to examine and find underlying patterns in these complex diseases. By using these techniques, it is possible to separate groups, identify the causes of such separation, and create disease prediction models. Clinical trials, mostly using cross-sectional Magnetic Resonance Imaging (MRI) data, have extensively looked into the use of MRI for the early identification of AD and MCI. On the other hand, longitudinal studies follow the same subjects over an extended period, giving researchers the chance to investigate cross-sectional trends as well as the development of the disease. Three different techniques are put forth in this study for the analysis and assessment of the structural data found in longitudinal MRI scans. Without considering any other diagnostic measures, this information is used to forecast the progression of those who have been diagnosed with MCI. These techniques utilize Hidden Markov Models (HMMs), which capitalize on the advantages of Support Vector Machine (SVM) classifiers.

@article{Ajabani2023,
  title     = {Predicting Alzheimer's Progression in Mild Cognitive Impairment: Longitudinal MRI with HMMs and SVM Classifiers},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Deep Himmatbhai Ajabani},
  doi       = {10.14569/IJACSA.2023.0141202},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141202}
}