Different types of brain illnesses can affect many parts of the brain at the
same time. Alzheimer's disease is a chronic illness characterized by brain cell
deterioration, which results in memory loss. Amnesia and ambiguity are two of the
most prevalent Alzheimer's disease symptoms, and both are caused by issues with
cognitive reasoning. This paper proposes several feature extractions as well as Machine
Learning (ML) algorithms for disease detection. The goal of this study is to detect
Alzheimer's disease using magnetic resonance imaging (MRI) of the brain. The
Alzheimer's disease dataset was obtained from the Kaggle website. Following that, the
unprocessed MRI picture is subjected to several pre-processing procedures. Feature
extraction is one of the most crucial stages in extracting important attributes from
processed images. In this study, wavelet and texture-based methods are used to extract
characteristics. Gray Level Co-occurrence Matrix (GLCM) is utilized for the texture
approach, and HAAR is used for the wavelet method. The extracted data from both
procedures are then fed into ML algorithms. The Support Vector Machine (SVM) and
Linear Discriminant Analysis (LDA) are used in this investigation. The values of the
confusion matrix are utilized to identify the best technique.
Keywords: Alzheimer, Confusion Matrix Values, Feature Extraction, HAAR, Magnetic Resonance Imaging.