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- W3120809784 abstract "The work focuses on the detection of Alzheimer’s disease (AD) at the very early stage. The disease progression is predicted at early stages like CN and MCI. The MRI data from AD neuro-imaging initiative (ADNI) along with the biomarkers are considered for the study. The segmented grey matter MRI is pre-processed for feature extraction and reduction using multilevel 2D DWT. The extracted wavelets along with biomarker and clinical data are applied to the prediction classifier-logistic regression which gave higher prediction accuracy than random forest for classifying between CN and MCI. The other classification metrics along with the statistical plot of prediction are obtained. Even though there are other literature’s regarding predictions of the disease, our work proves that the addition of biomarkers along with the MRI images improves the classification accuracy and prediction of the disease progression." @default.
- W3120809784 created "2021-01-18" @default.
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- W3120809784 date "2021-01-01" @default.
- W3120809784 modified "2023-09-26" @default.
- W3120809784 title "Predictive Analysis for Early Detection of Alzheimer’s Disease" @default.
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- W3120809784 doi "https://doi.org/10.1007/978-981-15-8530-2_56" @default.
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