Study of biomarker variation and severity prediction in dementia using intelligent system
by Ahana Priyanka; G. Kavitha
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 44, No. 1, 2024

Abstract: Precise detection of dementia biomarkers in the brain enables early understanding of pathology variations. Owing to which there is a need for studying different dementia biomarker in magnetic resonance (MR) image for its specific changes between normal and severity stages to categorise the prognostic difference. The present study is an attempt to utilise an optimised framework with fused radiomic and deep features based on least absolute shrinkage and selection operator (LASSO) using a hybrid meta-heuristic optimiser for classification. The investigation is attempted on Alzheimer's disease neuroimaging initiative (ADNI) database. The radiomic and deep features were extracted from the considered biomarkers and then fused. Further, the significant features were obtained using LASSO model. Then, those features were input to hybrid meta-heuristic optimiser with machine learning model for classification. From the result, it was identified that hippocampus, along with the brainstem, gave higher classification accuracy of 97.87% to identify prognostic differences for considered classes. Therefore, the quantifiable interpretation was claimed to improve clinical assessment.

Online publication date: Wed, 31-Jan-2024

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