Early Detection of Alzheimer’s Disease Using SVM, Random Forest & FNN Algorithms

Alzheimer’s disease is a brain disorder in which the central nervous system or the nerve tissues in the brain stop working or die, causing tissue damage. Symptoms include dementia, difficulty reading, writing, and performing various other tasks independently. Alzheimer’s disease, also known as AD, is an incurable disease at the current age. Though various medical procedures and tests, such as MMSE, CDR, eTIV, nWBV, etc., help in identifying the disease, there is no permanent cure for this disease. Hence, a comprehensive model for detecting the disease in an individual in the early stages of the disease is needed. To address this task, this paper uses a few machine learning algorithms to train the model to later classify the patient’s data to perform a medical data analysis and predict the patient’s status, whether the patient has AD, doesn’t have AD, or has the possibility of getting AD in the future. This model benefits the medical field by increasing the efficiency of results and decreasing the processing time of medical reports.