Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Prognosis of Dementia using Machine Learning

Author(s): Anu Saini, Sunita Kumari*, Ritik, Rajni and Sushma Hans

Pp: 80-91 (12)

DOI: 10.2174/9789815179125124010010

* (Excluding Mailing and Handling)


The brain is one of the most sensitive parts of the human body which transmits millions of signals every moment. Dementia is the most emerging brain health issue which involves memory loss, difficulty in problem-solving, handling complex tasks, etc. Dementia is a syndrome that causes a loss of mental ability. It affects memory, thinking, shape, comprehension, counting, reading ability, language, and judgment. Dementia affects millions of people and can be the leading cause of death. It is now the seventh leading cause of death worldwide, as well as one of the major causes of impairment and reliance on elderly people. There is no treatment for dementia at present. The importance of early detection and diagnosis in improving early and effective management is crucial. Predicting dementia in advance can lead us to a better life. To predict dementia, various Machine Learning models have been used. In this paper, Dementia is predicted on the basis of MRI Images, for this, three different datasets of MRI Images have been collected. Furthermore, for better prediction, various Machine learning models are used to predict dementia and validate the performance with statistical analysis like K-Nearest Neighbours, XG Boost, Support Vector Machine, Random Forest Algorithm (RFA), and Convolutional Neural Network (CNN). Out of all algorithms, Random Forest Algorithm and Convolutional Neural Network gave the best result with the accuracy of 93.2 and 99.9 respectively.

Keywords: Alzheimer's disease, CNN (Convolutional Neural Network), Dementia, Random forest algorithm.

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