Random forest prediction of Alzheimer’s disease using pairwise selection from time series data

Moore, Pearl; Lyons, Terry; Gallacher, John

doi:10.1371/journal.pone.0211558

Cited by 93 publications

(70 citation statements)

References 24 publications

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“…In particular, Moore et al. [8] achieved an mAUC score of 0.82 with a random forest classifier; Ghazi et al. [34] achieved an mAUC score of 0.7596 with an RNN; and Nguyen et al.…”

Section: Resultsmentioning

confidence: 99%

“…[22] Cognitive tests have also been widely used for early detection of AD. [8] Several commonly used tests, such as ADAS11 and ADAS13, are based on the Alzheimer's Disease Assessment Scale (ADAS), which is a brief cognitive test battery that assesses learning and memory, language production, language comprehension, constructional praxis, ideational praxis, and orientation. [23,24] ADAS11 scores range from 0 to 70, and ADAS13 scores range from 0 to 85, with higher scores indicating more advanced stages of AD.…”

Section: Introductionmentioning

confidence: 99%

“…AD takes a significant toll on patients' daily lives, causing a progressive decline in their cognitive abilities, including memory, language, behavior, and problem solving [3–6]. Changes to AD patients' cognitive abilities often start slowly and become more rapid over time [7,8]. Doctors and other caregivers monitor the progression of AD in patients by evaluating the degree of decline in the patients' cognitive abilities [9], which are often divided into 3 general categories: cognitively normal (NL), mild cognitive impairment (MCI), and dementia [5,10].…”

Section: Introductionmentioning

confidence: 99%

“…One study demonstrated a 4‐fold difference in the rate of ventricle enlargement in AD patients and normal controls over a six‐month interval [22]. Cognitive tests have also been widely used for early detection of AD [8]. Several commonly used tests, such as ADAS11 and ADAS13, are based on the Alzheimer's Disease Assessment Scale (ADAS), which is a brief cognitive test battery that assesses learning and memory, language production, language comprehension, constructional praxis, ideational praxis, and orientation [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Albright¹

2019

A&D Transl Res & Clin Interv

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Introduction There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years. Methods Data from 1737 patients were processed using the “All‐Pairs” technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). Results A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Discussion Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.

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“…In particular, Moore et al. [8] achieved an mAUC score of 0.82 with a random forest classifier; Ghazi et al. [34] achieved an mAUC score of 0.7596 with an RNN; and Nguyen et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Albright¹

2019

A&D Transl Res & Clin Interv

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“…We used MATLAB Statistics and Machine Learning Toolbox (MathWorks, Natick, MA) to train 4 popular algorithms (K-Nearest Neighbor, Random Forest, Support Vector Machine with Gaussian and cubic kernels) on 2 different gene panels -the 37 transcripts identi ed by LASSO in this study and the 26 transcripts identi ed by ltering in our previous study. While these algorithms have been used in other disease classi cation applications [39][40][41][42][43][44] , we implemented all 4 algorithms to determine which best suited our data. Speci c parameters for each algorithm are as follows:…”

Section: Model Trainingmentioning

confidence: 99%

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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Background: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n=94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n=40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)=0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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Prediction of CKD Stage 1 Using Three Different Classifiers

Thamizharasan¹,

Yamini²,

Shimola³

et al. 2021

Deep Learning Approaches to Cloud Security

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Random forest prediction of Alzheimer’s disease using pairwise selection from time series data

Cited by 93 publications

References 24 publications

Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Prediction of CKD Stage 1 Using Three Different Classifiers

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