Early diagnosis and detection of alzheimer disease using machine learning techniques | |
| Author | Asthana, Shantanu |
| Call Number | AIT RSPR no.IM-21-06 |
| Subject(s) | Alzheimer's disease--Early detection Alzheimer's disease--Diagnosis--Technological innovations Machine learning |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Alzheimer's disease (AD) is a progressive neuro degenerative ailment that causes increasing memory loss and impairment in daily activities as a result of brain cell damage. Dementia is one of the symptoms, and it impairs one's capacity to communicate, as well as one's thoughts, behaviour, and feelings. Because Alzheimer's disease is a progressive threat to individuals all over the world, early detection is critical. Earlier detection is promising since it can be helpful in predicting the state of a large number of patients they may encounter in the future. In this study, clinical data as well as a patient's demographic data were employed. AI-based Machine Learning (ML) approaches have become increasingly useful in the detection of Alzheimer's disease in recent years. In this study, different machine learning models such as Naive Bayesian, Logistic Regression, Decision Tree, Random Forest Classifier, and Support Vector Machine are trained on the Open Access Series of Imaging Studies (OASIS 2) dataset to predict the patients among the three classes of the output variable i.e. Converted, Non-Demented, Demented. The random forest model which has the highest accuracy of 93.69% and best performance is deployed in the system for the earlier prediction of Alzheimer's disease. The system provides a GUI where users can enter the values for the input features and the system displays the classification result using the best model. |
| Year | 2021 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharaporn Esichaikul; |
| Examination Committee(s) | Huynh, Trung Luong;Tripathi, Nitin Kumar; |
| Scholarship Donor(s) | AIT Partial Scholarship; |
| Degree | Research studies project report (M. Eng.) - Asian Institute of Technology, 2021 |