Abstract:
Within the field of biomedical engineering, a novel imputation model employing machine
learning techniques is proposed as a unique approach for the early diagnosis of Alzheimer's
disease (AD). Early identification is essential to postpone the progression of Alzheimer's
disease (AD) and lessen its burden on patients. AD is characterized by progressive cognitive
loss. The planned study is carried out in two stages. Phase one addresses a major obstacle in
early AD identification by introducing a state-of-the-art technique for imputing missing
values in clinical datasets. Data integrity is maintained using the imputation process, which
guarantees the preservation of statistical properties within each feature. Phase two involves
restructuring the clinical data and applying the proposed imputation model to impute the
missing values. The training of a classifier model that is intended to function with unique
labels based on patient prognosis comes next. With an accuracy rate of 92%, the imputation
model and classifier integration show a notable improvement in early AD identification. The
results highlight the usefulness of neuropsychological evaluations as reliable markers for the
early detection of Alzheimer's disease (AD), made possible by cutting-edge machine learning
techniques. This research contributes to the field of artificial intelligence by presenting a
robust imputation framework and its practical application in enhancing early diagnostic
capabilities in biomedical engineering.
