Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models

Frontiers in Public Health, Journal Year: 2022, Volume and Issue: 10

Published: March 3, 2022

Alzheimer's disease (AD) is the leading cause of dementia in older adults. There currently a lot interest applying machine learning to find out metabolic diseases like and Diabetes that affect large population people around world. Their incidence rates are increasing at an alarming rate every year. In disease, brain affected by neurodegenerative changes. As our aging increases, more individuals, their families, healthcare will experience memory functioning. These effects be profound on social, financial, economic fronts. its early stages, hard predict. A treatment given stage AD effective, it causes fewer minor damage than done later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Voting classifiers have been employed identify best parameters for prediction. Predictions based Open Access Series Imaging Studies (OASIS) data, performance measured with Precision, Recall, Accuracy, F1-score ML models. The proposed classification scheme can used clinicians make diagnoses these diseases. It highly beneficial lower annual mortality diagnosis algorithms. work shows better results validation average accuracy 83% test data AD. This score significantly higher comparison existing works.

Language: Английский

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Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions

Journal of Medical Systems, Journal Year: 2023, Volume and Issue: 47(1)

Published: Feb. 1, 2023

Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with aim developing disease prediction systems. One detection problems that AI researchers focused on dementia using methods. Numerous diagnostic systems based techniques early been proposed in literature. Few systematic literature reviews (SLR) conducted past. However, these SLR a single type data modality dementia. Hence, purpose this study conduct comprehensive evaluation ML-based considering different types modalities such as images, clinical-features, voice data. We collected articles from 2011 2022 keywords dementia, learning, feature selection, modalities, The selected were critically analyzed discussed. It was observed image driven models yields promising results terms compared other i.e., clinical feature-based Furthermore, highlighted limitations previously methods presented future directions overcome limitations.

Language: Английский

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Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions

Journal of Biomedical Informatics, Journal Year: 2022, Volume and Issue: 127, P. 104030 - 104030

Published: Feb. 17, 2022

Language: Английский

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Early diagnosis of Alzheimer's disease based on deep learning: A systematic review

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 146, P. 105634 - 105634

Published: May 17, 2022

Language: Английский

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A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(4), P. 2409 - 2429

Published: Jan. 3, 2023

Language: Английский

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Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(2), P. 101940 - 101940

Published: Jan. 24, 2024

Alzheimer's Disease (AD) is a worldwide concern impacting millions of people, with no effective treatment known to date. Unlike cancer, which has seen improvement in preventing its progression, early detection remains critical managing the burden AD. This paper suggests novel AD-DL approach for detecting AD using Deep Learning (DL) Techniques. The dataset consists pictures brain magnetic resonance imaging (MRI) used evaluate and validate suggested model. method includes stages pre-processing, DL model training, evaluation. Five models autonomous feature extraction binary classification are shown. divided into two categories: without Data Augmentation (without-Aug), CNN-without-AUG, (with-Aug), CNNs-with-Aug, CNNs-LSTM-with-Aug, CNNs-SVM-with-Aug, Transfer learning VGG16-SVM-with-Aug. main goal build best accuracy, recall, precision, F1 score, training time, testing time. recommended methodology, showing encouraging results. experimental results show that CNN-LSTM superior, an accuracy percentage 99.92%. outcomes this study lay groundwork future DL-based research identification.

Language: Английский

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Connecting the indispensable roles of IoT and artificial intelligence in smart cities: A survey

Journal of Information and Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

The pace of society development is faster than ever before, and the smart city paradigm has also emerged, which aims to enable citizens live in more sustainable cities that guarantee well-being a comfortable living environment. This been done by network new technologies hosted real time track activities provide solutions for incoming requests or problems citizens. One most often used methodologies creating Internet Things (IoT). Therefore, IoT-enabled research topic, consists many different domains such as transportation, healthcare, agriculture, recently attracted increasing attention community. Further, advances artificial intelligence (AI) significantly contribute growth IoT. In this paper, we first present concept, background components IoT-based city. followed up literature review on recent developments breakthroughs empowered AI techniques highlight current stage, major trends unsolved challenges adopting AI-driven IoT establishment desirable cities. Finally, summarize paper with discussion future recommendations direction domain.

Language: Английский

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RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Oct. 31, 2024

Detecting Alzheimer's disease typically involves a combination of medical and cognitive assessments, neuro imaging, sometimes genetic testing. Machine learning artificial intelligence (AI) techniques are being applied to analyze imaging data, information, clinical records develop predictive models for risk early detection. Many AI models, particularly deep lack interpretability. Understanding how model reaches particular diagnosis or prediction can be challenging, which is concern in the field where interpretability transparency crucial. CNNs learn features directly from data without prior feature engineering. While this an advantage, it may also limit exploration specific biomarkers known associated with disease. Medical images often require pre-processing steps, such as normalization, registration, segmentation, before feeding them into CNNs. The effectiveness depend on quality accuracy these steps. proposed methodology combines both CNN-based extraction integrates adaptive filtering leverage strengths each method. This hybrid approach lead improved detection by enhancing image extracting relevant diagnosis. allows network focus while out noise irrelevant information. Gaussian filter bilateral produce filter. Bilateral adapts local structure content. By using filtering, adaptively different regions image, optimizing smoothing enhancement process based features. more effective discriminative learning. Using traditional CNN approaches has got nearly 57.78% but 94.24%.

Language: Английский

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Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(4), P. 81 - 81

Published: March 28, 2024

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or sequence images to recognize content, has been used extensively across industries in recent years. However, the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV potential improve patient monitoring, system efficiencies, while reducing workload. In contrast previous reviews, we focus on end-user CV. First, briefly review categorize other (job enhancement, surveillance automation, augmented reality). We then developments hospital setting, outpatient, community settings. The advances monitoring delirium, pain sedation, deterioration, mechanical ventilation, mobility, surgical applications, quantification workload hospital, for events outside highlighted. To identify opportunities future also completed journey mapping at different levels. Lastly, discuss considerations associated with outline processes algorithm development testing limit expansion healthcare. This comprehensive highlights ideas expanded use

Language: Английский

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Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 3, 2024

Abstract Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various modalities for same patient, development multi-modal models leveraging these remains underexplored. This paper addresses this gap by proposing evaluating classification using 2D 3D MRI images amyloid PET scans uni-modal frameworks. Our findings demonstrate that volumetric data learn more effective representations than those only images. Furthermore, integrating multiple enhances model performance over single-modality approaches significantly. We achieved state-of-the-art on OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate our focuses crucial AD-related regions its predictions, underscoring potential to aid understanding disease’s causes.

Language: Английский

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