Using Support Vector Machine to Detect and Classify the Alzheimer Disease

Alzheimer's disease is a neurological condition that gradually affects memory, thinking, and reasoning abilities as well as daily functioning. The majority of people with this condition are elderly, particularly those over 65. The exact cause of this disease is still unknown, but it is now thought that it may be inherited, unintentional, or brought on by other factors. Actual assessment, CT examines, X-ray sweeps, and positron emission tomography, are a couple of techniques to analyze the disease, but X-ray checks are the most widely recognized one. Additionally, scanned images from MRI scans are used in this study. This study has established four groups for the condition: healthy, mildly demented, moderately demented, and very mildly demented. The most famous SVM technique is utilized in our proposed model along with a profound learning calculation because of its fast, trustworthy, and compelling activity. The dataset was obtained from Kaggle and uniformed through pre-processing. After the information was parted into preparing and testing datasets in a 80:20 proportion, tuner advancement was done to naturally pick various boundaries to improve the model. The ailment and its classification are then resolved utilizing the SVM grouping and the model's exactness is 99.4%. In order to diagnose the patient's condition and discover an appropriate course of treatment and therapy, it helps the doctors identify the disease and the degree of its dissemination.