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PROJECT TITLE:
Development of a Classification Model for Alzheimer's Disease using Machine Learning Techniques
Description:
In this project, Diagnosis of the Alzheimer’s disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) used to detect the disease. Here using FMRI, PET, and SPECT Datasets. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) used for feature selection and extraction. The resulting NMF-transformed sets of data are used to classify by means of an SVM-based classifier.
KEY ROLE:
Expertise in machine learning techniques.
Proficient in utilizing advanced imaging techniques (fMRI, SPECT, and PET).
Skilled in analyzing medical image databases using NMF & SVM techniques.
Proficiency in software tools including MATLAB, MS Office, and Visual Studio.
Achieved top classification results with Alzheimer’s disease data.
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