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Review on Computer Aided System Approach for Predictive Diagnosis of Neurological Disease

Neurological conditions in human brain affecting human body’s cognitive function leading to the mental diseases like Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, brain tumor, epilepsy, dementia, headache disorders, neuro infections, stroke and traumatic brain injuries. Alzheimer's disease is an irreversible neurological condition that affects the human body's cognitive functions. A previous diagnosis of Alzheimer's disease will aid in the treatment of the condition. Many mathematical and machine learning models have been used in studies supporting the disease. Magnetic resonance imaging (MRI) is a common method used to diagnose disease clinically. However, because to changes in its MRI samples and their stability in healthy people, it faces certain difficulties in diagnosis. Machine learning algorithms are currently being utilized to assess fundamental brain alterations in magnetic resonance imaging (MRI). Ensemble Learning (EL) also demonstrated its benefits by incorporating many models into the learning system's resilience. By forecasting the sickness, a machine learning system can help solve this problem. This paper presents a review of computer aided system approach for predictive diagnosis of neurological disease.