A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods

Pang, Zhen; Wang, Xiang; Wang, Xulong; Qi, Jun; Zhao, Zhigang; Gao, Yimin; Ye, Yun; Yang, Po

doi:10.1007/s11036-021-01834-1

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…A power equipment defect text processing method based on TensorFlow framework [7][8][9] is characterized in that the method first preprocesses the power defect text, then uses word2vec algorithm to convert the text into word vector [10][11][12], and finally constructs an improved convolution neural network model [13][14][15][16] under TensorFlow framework, and inputs the processed word vector to train the network to obtain the classifier. After the training, input the new power equipment defect text into the classifier and compare the actual results with the output results.…”

Section: Power Equipment Defect Text Mining Model Based On Semantic A...mentioning

confidence: 99%

Retracted: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm

Cao

Gao

et al. 2022

IET Communications

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The above article from IET Communications, published online on 17 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Power Equipment Defect Text Mining Model Based On Semantic A...mentioning

confidence: 99%

Retracted: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm

Cao

Gao

et al. 2022

IET Communications

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“…In the female group, there is nine relatively important feature, while the male group has five. In the paper [40], the author created a technological framework for a multiple modal data framework to unify the administration and exchange of ADNI data. Other deep learning based techniques to predict Alzheimer's [41][42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Singh¹,

Kumar²,

Tiwari³

2023

Computer Systems Science and Engineering

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Alzheimer's disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%-80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Alzheimer's disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer's. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer's disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.

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“…Deep learning models may give good estimations in the detection of breast cancer. 19 According to the general performance standards, LSTM (long short-term memory) and GRU (gated recurrent unit) have proven to be highly effective in producing beneficial outcomes. One reason might be that these two algorithms have internally recognised characteristics that have a substantial influence on shaping up the training performance, resulting in better accuracy than the other algorithms utilised in this study.…”

Section: Prediction Of Cancermentioning

confidence: 99%

A systematic review on Machine learning and Neural Network based models for disease prediction

Roobini,

Kavitha,

Karthik

2024

JIST

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Over the recent years, conventional artificial intelligence (AI) has witnessed a significant infusion of machine learning and neural networks, marking a substantial evolution in various domains due to their autonomous capacity for feature acquisition and remarkable efficiency. Particularly in the medical field, machine learning-based models have outperformed physicians, exhibiting greater accuracy. Diseases such as cancer, Alzheimer's, dyslexia, skin diseases, and heart diseases have become focal points in medical research. Several deep learning methods, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, Logistic Regression, Decision Tree, and Recurrent Neural Networks (RNN), play crucial roles in disease prediction. This survey emphasizes the critical analysis of which deep learning models achieve higher accuracy in predicting specific diseases. The objective is to shed light on existing shortcomings in disease prediction and propose potential remedies for future improvements. Results indicate that Convolutional Neural Networks excel in predicting heart and Alzheimer's diseases, as well as breast cancer. Support Vector Machines demonstrate effectiveness in cancer prediction, while logistic regression proves adept at predicting dyslexia, and decision trees emerge as a favorable choice for skin diseases. Looking ahead, the integration of digital twins for predictive analytics, facilitating the simulation and modeling of disease progression based on individual patient characteristics, and leveraging blockchain for secure storage and sharing of health data represent promising avenues for future developments.

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A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods

Cited by 11 publications

References 41 publications

Retracted: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm

Retracted: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

A systematic review on Machine learning and Neural Network based models for disease prediction

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