Pre-Detection of Dementia Using Machine Learning Mechanism

Abstract

Dementia is a neurological disorder that affects millions of people worldwide. Dementia is also not a normal part of aging, and there is no cure or effective treatment for it. The number of persons suffering from dementia is rapidly increasing. According to the 2015 World Alzheimer Report, there are 46.8 million people worldwide who have been diagnosed with dementia, with that figure anticipated to rise to 74.7 million by 2030 and 131.5 million by 2050. The number of people diagnosed with dementia in Sri Lanka is continuously increasing. According to government projections, there are already more than 0.2 million dementia patients, with that number anticipated to climb to 0.5 million by 2050. As a result, dementia has emerged as a serious medical condition that needs to be addressed for the sake of society's well-being. Dementia is a difficult condition to manage, and it must be dealt with quickly. According to the World Alzheimer’s Association, Dementia is one of the most financially costly diseases in the world since there is no proper treatment to cure the disease and the cause of the disease is not identified correctly. Recently there is a strong interest in machine learning mechanism which provides a better classification accuracy than the conventional classification methods. Based upon the recent studies we design and perform some experiments to investigate the possibility of early diagnosis of dementia from machine learning mechanism using the clinical data. In this project, we compare machine learning algorithms to clinical data from dementia patients in order to develop a better approach for detecting dementia at an early stage. We primarily use three main advanced machine learning algorithms: SVM, decision tree, and random forest. We train the preprocessed clinical data with the above advance algorithms and takes the highest accurate algorithm after comparing the results along with the confusion matrix of each other. After comparing the confusion matrix results, we choose random forest algorithm as the most accurate machine learning algorithm and it used to trained machine learning model. In this project we developed a mobile application for the public people and this mobile application is integrated with the machine learning model. So it is very helpful for the general public to identify their day-to-day mental capabilities with this mobile application. This will evaluate the person's dementia level and aid in the early detection of dementia. As a result, this research will be a significant step forward in the prevention of dementia in Sri Lanka. We expect that this dissertation will help researchers to get better understand about how machine learning can be used in early dementia detection.