scholarly journals A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological Measures

2019 ◽  
Author(s):  
Massimiliano Grassi ◽  
Nadine Rouleaux ◽  
Daniela Caldirola ◽  
David Loewenstein ◽  
Koen Schruers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Disease Stage ◽  
Machine Learning Algorithm

ABSTRACTBackgroundDespite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to full AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to full AD.MethodsThe algorithm was developed using the open dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm.ResultsThe final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity.DiscussionThe algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

scholarly journals A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach

2018 ◽  
Vol 31 (07) ◽  
pp. 937-945 ◽  
Author(s):  
Massimiliano Grassi ◽  
David A. Loewenstein ◽  
Daniela Caldirola ◽  
Koen Schruers ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Algorithm

ABSTRACTBackground:In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.Methods:We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.Results:Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).Conclusions:These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.

scholarly journals Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052093688
Author(s):  
Daehyuk Yim ◽  
Tae Young Yeo ◽  
Moon Ho Park
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Dysfunction ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Kappa Statistics ◽  
Machine Learning Algorithm ◽  
Neuropsychological Screening ◽  
Dementia Screening

Objective To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. Methods This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant’s caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen’s kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. Results The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. Conclusion This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

scholarly journals MACHINE LEARNING MODEL BASED ON A PANEL OF PLASMA PROTEINS PREDICTS PROGRESSION FROM MILD COGNITIVE IMPAIRMENT TO DEMENTIA DUE TO ALZHEIMER’S DISEASE WITHIN FOUR YEARS

2021 ◽  
Author(s):  
Daniella Araujo ◽  
Paulo Caramelli ◽  
Nivio Ziviani ◽  
Karina Gomes ◽  
Adriano Veloso ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Plasma Proteins ◽  
Risk Model ◽  
Disease Stage ◽  
Lifestyle Interventions ◽  
Increased Risk

Background: Alzheimer’s disease (AD) is a pathological process that begins many years prior to the emergence of symptoms. Thus, an effective risk model for AD should aim at detecting at-risk individuals in the prodromal stage, when treatments and lifestyle interventions are more likely to be successful, and should be minimally invasive and inexpensive to allow widespread applicability. Objectives: To develop a machine learning based blood panel in individuals with mild cognitive impairment (MCI) to predict increased risk for progression to AD dementia. Methods: We created over one billion models to predict the probability of conversion from MCI to dementia due to AD, and chose the best performing one. We used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data of 380 MCI individuals in the baseline visit, from which 177 converted to AD dementia. Results: The best performing model uses 12 plasma proteins (out of 146 possibilities), giving an average AUC of 0.91, accuracy of 0.92, sensitivity of 0.97 and specificity of 0.85. Conclusions: We were able to predict AD dementia conversion within four years in MCI individuals, a disease stage in which treatments and lifestyle interventions are more likely to be successful. Further studies in independent cohorts are needed to validate this panel.

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer’s Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach

2021 ◽  
pp. 1-10
Author(s):  
Jennifer Li ◽  
Andres M. Bur ◽  
Mark R. Villwock ◽  
Suraj Shankar ◽  
Gracie Palmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Subjective Data ◽  
Disease States ◽  
Olfactory Testing

Background: Olfactory dysfunction (OD) is an early symptom of Alzheimer’s disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD. Objective: This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD. Methods: Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array –AROMA; Sniffin’ Sticks Screening 12 Test –SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states. Results: Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p <  0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375. Conclusion: OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

scholarly journals A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229460
Author(s):  
Sylvester Olubolu Orimaye ◽  
Karl Goodkin ◽  
Ossama Abid Riaz ◽  
Jean-Maurice Miranda Salcedo ◽  
Thabit Al-Khateeb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment
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