A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors

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. nondementia) 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.

Discovery of Intentional Self-Harm Patterns from Suicide and Self-Harm Surveillance Reports

“…The results of the self-harm risk classification using the SVM, RF, MLP, DT, and kNN techniques were satisfactory, with an accuracy of more than 87%, proving that these techniques are suitable for use in the classification of self-harm risks [ 14 – 25 ]. The RF technique is most effective because it reduces overfitting in decision trees and improves accuracy and flexibility in problem classification, which corresponds to high accuracy according to prior research [ 17 – 19 , 21 – 24 ].…”

“…This step classified the risk for hospital admission from self-harm using popular ML techniques for suicidality risk classification to compare the effectiveness of techniques for managing missing data. We utilized the following techniques: support vector machine (SVM) [ 14 – 20 ], random forest (RF) [ 18 , 19 , 21 – 24 ], multilayer perceptron (MLP) [ 18 – 20 ], DT [ 7 , 9 , 18 ], and k-nearest neighbors (kNN) [ 25 ].…”

“…2) Classifying the risk of self-harm This step classified the risk for hospital admission from selfharm using popular ML techniques for suicidality risk classification to compare the effectiveness of techniques for managing missing data. We utilized the following techniques: support vector machine (SVM) [14][15][16][17][18][19][20], random forest (RF) [18,19,[21][22][23][24], multilayer perceptron (MLP) [18][19][20], DT [7,9,18], and k-nearest neighbors (kNN) [25]. These steps were performed using a 2.5-GHz Dual-Core Intel Core i5 computer with 8 GB of RAM using Python version 2.7.16.…”

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

“…NLP was used only in studies which included patient clinical notes as one of the features; these comprised 6% of all studies. [58][59][60][61]…”

“…Only 4 articles considered clinical notes, primarily patient medical history and diagnosis details documented by clinicians. [58][59][60][61] Thirty studies (47%) were categorized as Clinical only and 34 (53%) as Clinical þ Imaging. Figure 4 shows the relationship between the nature of data access restrictions (publicly available or restricted) and the category of AD dementia features (Clinical only or Clinical þ Imaging).…”