A Gender-Neutral Approach to Detect Early Alzheimer’s Disease Applying a Three-layer NN
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 3
Abstract
Early diagnosis of the neurodegenerative, irreversible disease Alzheimer’s is crucial for effective disease management. Dementia from Alzheimer’s is an agglomerated result of complex criteria taking roots at both medical, social, educational backgrounds. There being multiple predictive features for the mental state of a subject, machine learning methodologies are ideal for classification due to their extremely powerful feature-learning capabilities. This study primarily attempts to classify subjects as having or not having the early symptoms of the disease and on the sidelines, endeavors to detect if a subject has already transformed towards Alzheimer’s. The research utilizes the OASIS (Open Access Series of Imaging Studies) longitudinal dataset which has a uniform distribution of demented, nondemented subjects and establishes the use of novel features such as socio-economic status and educational background for early detection of dementia, proven by performing exploratory data analysis. This research exploits three data-engineered versions of the OASIS dataset with one eliminating the incomplete cases, another one with synthetically imputed data and lastly, one that eliminates gender as a feature—eventually producing the best results and making the model a gender-neutral unique piece. The neural network applied is of three layers with two ReLU hidden layers and a third softmax classification layer. The best accuracy of 86.49% obtained on cross-validation set upon trained parameters is greater than traditional learning algorithms applied previously on the same data. Drilling down to two classes namely demented and non-demented, 100% accuracy has been remarkably achieved. Additionally, perfect recall and a precision of 0.8696 for the ‘demented’ class have been achieved. The significance of this work consists in endorsing educational, socio-economic factors as useful features and eliminating the gender-bias using a simple neural network model without the need for complete MRI tuples that can be compensated for using specialized imputation methods.
Authors and Affiliations
Shithi Maitra, Tonmoy Hossain, Abdullah Al-Sakin, Sheikh Inzamamuzzaman, Md. Mamun Or Rashid, Syeda Shabnam Hasan
Keywords
Related Articles
- EP ID EP499639
- DOI 10.14569/IJACSA.2019.0100368
- Views 63
- Downloads 0
Electrooculogram Signals Analysis for Process Control Operator Based on Fuzzy c-Means
Biomedical signals of human can reflect the body's task load, fatigue and other psychological information. Compared with other biomedical signals, electrooculogram (EOG) has higher amplitude, less interference, and is ea...
Detection of SQL Injection Using a Genetic Fuzzy Classifier System
SQL Injection (SQLI) is one of the most popular vulnerabilities of web applications. The consequences of SQL injection attack include the possibility of stealing sensitive information or bypassing authentication procedur...
LOD Explorer: Presenting the Web of Data
The quantity of data published on the Web according to principles of Linked Data is increasing intensely. However, this data is still largely limited to be used up by domain professionals and users who understand Linked...
Performance Evaluation of Network Gateway Design for NoC based System on FPGA Platform
Network on Chip (NoC) is an emerging interconnect solution with reliable and scalable features over the System on Chip (SoC) and helps to overcome the drawbacks of bus-based interconnection in SoC. The multiple cores or...
Development Trends of Online-based Aural Rehabilitation Programs for Children with Cochlear Implant Coping with the Fourth Industrial Revolution and Implication in Speech-Language Pathology
The Korea Research Foundation selected the miniaturization and development of home care devices as the future promising technologies in the biotechnology (BT) area along with the Fourth Industrial Revolution. Accordingly...