Predicting the transition from Mild Cognitive Impairment to Alzheimer’s disease using Cognitive tests and MRI measures of Demographic Data with an Ensemble Model
Keywords:
Machine Learning, Ensemble, ADNIMERGE, Feature selection, principal component analysis, Alzheimer's disease (AD)Abstract
Dementia, one of the most dreaded illnesses, has an enormous annual impact on health and social care expenses worldwide than cancer and chronic heart disease. Despite the lack of a treatment or standardized clinical test, using machine learning techniques to identify people at risk of developing Dementia could represent a new step toward proactive management. Cerebrospinal fluid (CSF), positron emission tomography (PET) scans, magnetic resonance imaging (MRI), biological markers (biomarkers), clinical scans, and neuropsychological therapy are all integrated to track the development of early Alzheimer's disease (AD) and moderate cognitive impairment (MCI). Early detection of Alzheimer's disease (AD) is crucial for controlling the illness, assistance, and the accessibility of healthcare resources. This study focused on the detection rate and false positive rate of a disease determined from ADNI-ADNIMERGE demographic data using a variety of machine learning techniques, including KNN, SVM, RF, NB, LOGISTIC, and Ensembled: LOGISTIC-PCA, SVM, KNN as the final algorithm with feature selection and hyper-tuning parameter optimization. Performed a comparison analysis between machine learning methods and ensemble model. Ensemble model showed best results with change in biomarker and baseline biomarker of disease detection rate, false positive rate and test accuracy 92%, 90% of AD respectively.
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