The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

El–Sappagh, Shaker; Abuhmed, Tamer; Alouffi, Bader; Sahal, Radhya; Abdelhade, Naglaa; Saleh, Hager

doi:10.1155/2021/8439655

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…The normalization of node samples was adopted to eliminate the influences of sample points on the number of samples [17], and then, the calculation method of the target function of data sets was expressed by…”

Section: Svm Prediction Modelmentioning

confidence: 99%

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Shang

Zhang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer’s disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer’s disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group ( P < 0.01 ). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group ( P < 0.001 ). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score ( R 2 = 0.1702 , 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001 ). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model ( P < 0.01 ). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model ( P < 0.01 ). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.

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Section: Svm Prediction Modelmentioning

confidence: 99%

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Shang

Zhang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Different models have been compared with the proposed model: six regular ML models, CNN, and LSTM. Six regular ML models such as DT [ 33 ], LR [ 34 ], KNN [ 35 ], RF [ 36 ], SVM [ 13 ], and NB [ 37 ] were used to compare with proposed model. The long short-term memory (LSTM) model has five layers which are (1) an embedding layer, (2) hidden layer, (3) dropout layer, (4) flatten layer, and (5) an output layer.…”

Section: The Proposed System Of Detecting Covid-19 Fake Newsmentioning

confidence: 99%

“…Six regular ML models such as DT [ 33 ], LR [ 34 ], KNN [ 35 ], RF [ 36 ], SVM [ 13 ], and NB [ 37 ] were used to compare with proposed model.…”

Section: The Proposed System Of Detecting Covid-19 Fake Newsmentioning

confidence: 99%

“…As a result of the research direction in content analysis, the research organizations start raising funding to provide novel solutions to combat COVID-19 in terms of analyzing the misleading information about the COVID-19 pandemic [ 5 – 10 ]. Recently, machine learning and deep learning are playing a vital role in different areas such as sentiment analysis [ 11 , 12 ]; Alzheimer detection [ 13 ]; prediction cancer [ 14 ], and others [ 15 , 16 ]. The researchers have utilized the collected datasets related to COVID-19 through social media to evaluate their proposed approaches [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Hybrid Deep Learning Model to Detect COVID‐19 Misleading Information

Alouffi

Alharbi

Sahal

et al. 2021

Computational Intelligence and Neuroscience

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Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

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“…For all of these reasons, there is an ongoing need for a reliable and accurate system that can be used to help in the early detection and diagnosis of BC diseases to reduce the number of deaths. In the field of medical analysis, machine-learning (ML) algorithms can be applied extensively [ 2 ], for example, predicting COVID-19 [ 3 ], predicting Alzheimer's progression[ 4 ], predicting chronic diseases [ 5 ], predicting liver disorders [ 6 ], heart disease [ 7 ], cancer [ 8 ], and others [ 9 , 10 ]. ML and deep learning (DL) play a significant role in solving health problems and identifying diseases, such as cancer prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting Breast Cancer Based on Optimized Deep Learning Approach

Saleh

ghany²,

Alyami

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in making diagnosis decisions. This paper proposed the optimized deep recurrent neural network (RNN) model based on RNN and the Keras–Tuner optimization technique for breast cancer diagnosis. The optimized deep RNN consists of the input layer, five hidden layers, five dropout layers, and the output layer. In each hidden layer, we optimized the number of neurons and rate values of the dropout layer. Three feature-selection methods have been used to select the most important features from the database. Five regular ML models, namely decision tree (DT), support vector machine (SVM), random forest (RF), naive Bayes (NB), and K-nearest neighbor algorithm (KNN) were compared with the optimized deep RNN. The regular ML models and the optimized deep RNN have been applied the selected features. The results showed that the optimized deep RNN with the selected features by univariate has achieved the highest performance for CV and the testing results compared to the other models.

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The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

Cited by 12 publications

References 24 publications

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

An Optimized Hybrid Deep Learning Model to Detect COVID‐19 Misleading Information

Predicting Breast Cancer Based on Optimized Deep Learning Approach

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