Alzheimer's disease prediction using machine learning techniques and principal component analysis (PCA)
| Autor: | M. Sudharsan, G. Thailambal |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
010302 applied physics
Computer science business.industry Feature selection 02 engineering and technology General Medicine Disease Human brain 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Support vector machine medicine.anatomical_structure Neuroimaging Kernel (statistics) 0103 physical sciences Feature (machine learning) medicine Artificial intelligence 0210 nano-technology business computer Extreme learning machine |
| Zdroj: | Materials Today: Proceedings. 81:182-190 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2021.03.061 |
| Popis: | Alzheimer's disease (AD) is a neurodegenerative disease of the human brain that affects neurotransmitters, tissue, and neurons that impair the senses, memories, and behaviors. Still, now there is no remedy for Alzheimer's disease. Even so, prescribed drugs can help reduce the development of the disease. That's why Alzheimer's early detection is very essential for treatment, and further research. Very limited numbers of trained samples and the higher volume of feature descriptions are the major difficulties in early diagnosis of Alzheimer's disease using different classification strategies. In this article, we proposed and related Alzheimer's disease early diagnostic method using Mild Cognitive Impairment (MCI), Structural Magnetic Resonance (sMR) imaging for AD-discrimination and healthy control participants (HC) with Import Vector Machine (IVM), Regularized Extreme Learning Machine (RELM) and a Support vector machine (SVM).The greedy score-based strategy for choosing essential function vectors is used. Furthermore, a discriminatory, kernel-based method is taken to treat dynamic data transformations. For volume sMR scan image data from Alzheimer's disease neuroimaging initiative (ADNI) repositories, we compare the performance of these classification models. An ADNI datasets experimental study reveals that RELM can greatly enhance the accuracy for classification of AD from MCIs as well as HC individuals along with feature selection methodology. |
| Databáze: | OpenAIRE |
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