PREDICTIVE PRECISION: AI - DRIVEN DEMENTIA PROGNOSIS & REHAB PREP
Author(s):
Pariyada Sai Jyotshna, Dr.G.Venkata Rami Reddy
Keywords:
Dementia prediction, machine learning, parameter optimization, transfer learning.
Abstract
Dementia is a neurodegenerative disease that causes a progressive decline in memory, thinking, and the ability to execute daily activities. Emotional disorders, language disorders, and reduced mobility are additional prevalent symptoms; however, self-consciousness is unaffected. Dementia is irreversible, and medicine can only delay the degeneration, not stop it. Nonetheless, if dementia could be foretold, its onset could be avoided. Thus, we proposes a revolutionary transfer-learning machine-learning model to predict dementia from magnetic resonance imaging data. In training, k-fold cross-validation and various parameter optimization algorithms were used to increase prediction accuracy. Synthetic minority oversampling was used for data augmentation. The final model achieved good accuracy, superior to that of competing methods on the same data set. This study’s model facilitates the early diagnosis of dementia, which is key to arresting neurological deterioration from the disease, and is useful for under served regions where many do not have access to a human physician. In the future, the proposed system can be used to plan rehabilitation therapy programs for patients.
Article Details
Unique Paper ID: 167315

Publication Volume & Issue: Volume 11, Issue 3

Page(s): 812 - 819
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