Class Imbalance Applied to Medical Neuroimaging for Classification of Alzheimer’s Disease

Autor:	Rajeswari Kr Kruthika
Rok vydání:	2020
Předmět:	business.industry Computer science Feature vector Public Health Environmental and Occupational Health Pattern recognition Voxel-based morphometry Disease computer.software_genre Class (biology) Random forest Support vector machine Neuroimaging Voxel Artificial intelligence business computer
Zdroj:	Indian Journal of Public Health Research & Development.
ISSN:	0976-5506 0976-0245
DOI:	10.37506/ijphrd.v11i7.10119
Popis:	Class imbalance is an issue that naturally occurs when a database is sparse or incomplete. This can occur in medical diagnostics when a large percentage of tests ran to return negative results rather than a positive. Classifification models are sensitive to an imbalanced training set, and training on one can cause undesirable biases. This work presents an overview of the effffects of class imbalance on classifification models in Alzheimer’s detection utilizing voxel based-morphometry (VBM). MRI scans are processed by FreeSurfer where cerebral volumetric and thickness are taken as feature vectors. The effcts of class imbalances on multiple machine learning models were compared to one another. Furthermore, different biomarkers were studied for their effect on different metrics of trained models. The classifification models were trained to detect the following categories: Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal controls (NC). SVM, KNN, MLP, Random Forest, etc. algorithms were evaluated for the prediction analysis. It was observed that class imbalance did not produce any significant effects on the disease classification process.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ffd3ae08c96bcaad93dbf6fb716c7bf2 https://doi.org/10.37506/ijphrd.v11i7.10119 Zobrazit plný text záznamu