An Early Prediction and Detection of Alzheimer's Disease : A Comparative study using machine learning algorithms- Literature survey

Computer Engineering

Alzheimer's disease has recently been a major concern. Over 45 million people worldwide are afflicted by this condition. Alzheimer's is a degenerative brain disease with an unknown cause and path physiology that primarily affects older adults. Alzheimer's disease is mostly brought on by dementia, which gradually destroys brain cells. People lost their ability to read, think, and do many other things as a result of this sickness. A machine learning system can solve this problem by anticipating the sickness. Finding dementia in a range of persons is the main objective. This study presents the results and analysis from various machine learning models used to detect dementia. The Open Access Series of Imaging Studies (OASIS) dataset was used to construct the system The dataset is small while being tiny. Many machine learning models have been used and investigated. Support vector machines, logistic regression, decision trees, and random forests are examples of prediction approaches. Both with and without fine-tuning, the system has been used. The support vector machine is shown to generate the best results of all the models when the results are compared. It is the most effective in identifying dementia among a large number of people. dementia is one of them. Although there are many machine learning systems, their conclusions are frequently erroneous and inconsistent. Additionally, they battle with worries about both overfitting and underfitting. Therefore, we have created a model that may identify Alzheimer's disease early using machine learning to aid medical technicians. If someone has Alzheimer's disease, it will prove it and confirm it.

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