A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 8
Abstract
Dementia is considered one of the greatest global health and social care challenges in the 21st century. Fortunately, dementia can be delayed or possibly prevented by changes in lifestyle as dictated through known modifiable risk factors. These risk factors include low education, hypertension, obesity, hearing loss, depression, diabetes, physical inactivity, smoking, and social isolation. Other risk factors are non-modifiable and include aging and genetics. The main goal of this study is to demonstrate how machine learning methods can help predict dementia based on an individual’s modifiable risk factors profile. We use publicly available datasets for training algorithms to predict participant’ s cognitive state diagnosis, as cognitive normal or mild cognitive impairment or dementia. Several approaches were implemented using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) longitudinal study. The best classification results were obtained using both the Lancet and the Libra risk factor lists via longitudinal datasets, which outperformed cross-sectional baseline datasets. Moreover, using only data of the most recent visits provided even better results than using the complete longitudinal set. A binary classification (dementia vs. non-dementia) yielded approximately 92% accuracy, while the full multi-class prediction performance yielded to a 77% accuracy using logistic regression, followed by random forest with 92% and 70% respectively. The results demonstrate the utility of machine learning in the prediction of cognitive impairment based on modifiable risk factors and may encourage interventions to reduce the prevalence or severity of the condition in large populations.
Authors and Affiliations
Reem Bin-Hezam, Tomas E. Ward
Keywords
Related Articles
- EP ID EP626595
- DOI 10.14569/IJACSA.2019.0100820
- Views 68
- Downloads 0
Image Denoising using Adaptive Thresholding in Framelet Transform Domain
Noise will be unavoidable during image acquisition process and denosing is an essential step to improve the image quality. Image denoising involves the manipulation of the image data to produce a visually high quality im...
Repository System for Geospatial Software Development and Integration
The integration of geospatial software components has recently received considerable attention due to the need for rapid growth of GIS application and development environments. However, finding appropriate source code co...
Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems
Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, devel...
Solving Word Tile Puzzle using Bee Colony Algorithm
In this paper, an attempt has been made to solve the word tile puzzle with the help of Bee Colony Algorithm, in order to find maximum number of words by moving a tile up, down, right or left. Bee Colony Algorithm is a ty...
A Robust Method for Diagnostic Energetic System with Bond Graph
Surveillance and supervision systems have a major role in insuring the safety and availability of industrial equipments and installations. Default detection and diagnosis is highly important to facilitate the planning an...