A structured approach to ensemble learning for Alzheimer's disease prediction

Seeley, Matthew; Clement, Mark J.; Giraud‐Carrier, Christophe; Snell, Quinn; Bodily, Paul; Fujimoto, Stanley

doi:10.1145/2649387.2660797

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Roland Assam et al, based on the conditional random field (CRF), used the extracted sample feature vector to capture the latent features of the freeze of gait (FOG) time series of Parkinson's disease patients [11], analyzed the motion time series data of Parkinson's disease patients, and analyzed the patient's freezing of gate state (FOG) for effective prediction. Matthew Seeley et al used a structured method of integrated learning to compare the accuracy of multiple model combinations for predicting Alzheimer's disease (AD) [12]. Finally, they obtained the characteristic attributes that affect the prediction results.…”

Section: Related Workmentioning

confidence: 99%

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals. In order to predict the health risks of the elderly without relying on professional medical testing equipment in the application scenarios of nursing homes, we use the datasets collected by non-professional medical testing equipment. Based on transfer learning and lightweight neural networks, we propose a disease risk prediction model, Diplin (disease risk prediction model based on lightweight neural network), applied to nursing homes. This model achieved 98% accuracy, 97% precision, 96% recall, 95% specificity, 97% F1 score, and 1.0 AUC (area under ROC curve) value on the validation set. The experimental results show that in the application scenario of nursing homes, the Diplin model can provide practical support for predicting the health risks of the elderly, and this model can be run directly on the tablet.

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Section: Related Workmentioning

confidence: 99%

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Conversely, overfitting occurs when irrelevant fluctuations in the training data are also captured by the neural network, resulting in lower generalization [336]. Ensemble learning has been widely applied in medical image classification [282,337] and the classification of AD and related diseases [338,339].…”

Section: Ensemble Learningmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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A structured approach to ensemble learning for Alzheimer's disease prediction

Abstract: This research employs an exhaustive search of different attribute selection algorithms in order to provide a more structured approach to learning design for prediction of Alzheimer's clinical dementia rating (CDR).

Cited by 2 publications

References 3 publications

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

A Survey of Deep Learning for Alzheimer’s Disease

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