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Anderberg, Peter, ProfessorORCID iD iconorcid.org/0000-0001-9870-8477
Publications (10 of 73) Show all publications
Javeed, A., Anderberg, P., Ghazi, A. N., Noor, A., Elmståhl, S. & Sanmartin Berglund, J. (2024). Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia. Frontiers in Bioengineering and Biotechnology, 11, Article ID 1336255.
Open this publication in new window or tab >>Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia
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2024 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1336255Article in journal (Refereed) Published
Abstract [en]

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia.

Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency.

Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535.

Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2024
Keywords
dementia, voting classifier, F-score, machine learning, feature selection
National Category
Computer Sciences Geriatrics
Research subject
Software Engineering; Computer Science; Applied Health Technology
Identifiers
urn:nbn:se:bth-25877 (URN)10.3389/fbioe.2023.1336255 (DOI)2-s2.0-85182656352 (Scopus ID)
Projects
National E-Infrastructure for Aging Research (NEAR)
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-02-02Bibliographically approved
Flyborg, J., Renvert, S., Anderberg, P., Larsson, T. & Sanmartin Berglund, J. (2024). Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health. Clinical Oral Investigations, 28(1), Article ID 8.
Open this publication in new window or tab >>Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health
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2024 (English)In: Clinical Oral Investigations, ISSN 1432-6981, E-ISSN 1436-3771, Vol. 28, no 1, article id 8Article in journal (Refereed) Published
Abstract [en]

Objectives: The study aimed to investigate how the objective use of a powered toothbrush in frequency and duration affects plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm in elderly individuals with MCI. A second aim was to compare the objective results with the participants’ self-estimated brush use. Materials and methods: Objective brush usage data was extracted from the participants’ powered toothbrushes and related to the oral health variables plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm. Furthermore, the objective usage data was compared with the participants’ self-reported brush usage reported in a questionnaire at baseline and 6- and 12-month examination. Results: Out of a screened sample of 213 individuals, 170 fulfilled the 12-month visit. The principal findings are that despite the objective values registered for frequency and duration being lower than the recommended and less than the instructed, using powered toothbrushes after instruction and information led to improved values for PI, BOP, and PPD ≥ 4 mm in the group of elderly with MIC. Conclusions: Despite lower brush frequency and duration than the generally recommended, using a powered toothbrush improved oral health. The objective brush data recorded from the powered toothbrush correlates poorly with the self-estimated brush use. Clinical relevance: Using objective brush data can become one of the factors in the collaboration to preserve and improve oral health in older people with mild cognitive impairment. Trial registration: ClinicalTrials.gov Identifier: NCT05941611, retrospectively registered 11/07/2023. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2024
Keywords
Elderly individuals, Mild cognitive impairment, Oral health, Powered toothbrush
National Category
Dentistry
Identifiers
urn:nbn:se:bth-25835 (URN)10.1007/s00784-023-05407-2 (DOI)2-s2.0-85180240432 (Scopus ID)
Available from: 2023-12-30 Created: 2023-12-30 Last updated: 2023-12-30Bibliographically approved
Idrisoglu, A., Moraes, A. L., Anderberg, P. & Sanmartin Berglund, J. (2023). Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research, 25, Article ID e46105.
Open this publication in new window or tab >>Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review
2023 (English)In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 25, article id e46105Article, review/survey (Refereed) Published
Abstract [en]

Background: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. © 2023 Journal of Medical Internet Research. All rights reserved.

Place, publisher, year, edition, pages
JMIR Publications, 2023
Keywords
diagnosis, digital biomarkers, machine learning, monitoring, voice features, voice-affecting disorder, Humans, Monitoring, Physiologic, human, physiologic monitoring
National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:bth-25253 (URN)10.2196/46105 (DOI)001048954300007 ()2-s2.0-85165520794 (Scopus ID)
Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2024-02-20Bibliographically approved
Javeed, A., Saleem, M. A., Moraes, A. L., Ali, L., Sanmartin Berglund, J. & Anderberg, P. (2023). Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning. Applied Sciences, 13(8), Article ID 5188.
Open this publication in new window or tab >>Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 8, article id 5188Article in journal (Refereed) Published
Abstract [en]

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a ?(2) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (?(2)_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model ?(2)_RF achieved the highest accuracy of 94.59%. The proposed model ?(2)_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model ?(2)_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (?(2)).

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
heart morality, feature ranking, random forest, imbalance classes
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:bth-24631 (URN)10.3390/app13085188 (DOI)000980955700001 ()
Available from: 2023-05-26 Created: 2023-05-26 Last updated: 2023-05-26Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Idrisoglu, A., Ali, L., Rauf, H. T. & Anderberg, P. (2023). Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification. Biomedicines, 11(2), Article ID 439.
Open this publication in new window or tab >>Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
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2023 (English)In: Biomedicines, E-ISSN 2227-9059, Vol. 11, no 2, article id 439Article in journal (Refereed) Published
Abstract [en]

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
dementia, feature fusion, machine learning, imbalance classes
National Category
Computer Sciences Geriatrics
Research subject
Applied Health Technology; Computer Science
Identifiers
urn:nbn:se:bth-24292 (URN)10.3390/biomedicines11020439 (DOI)000938259800001 ()2-s2.0-85148904251 (Scopus ID)
Projects
National E-Infrastructure for Aging Research (NEAR)
Available from: 2023-02-16 Created: 2023-02-16 Last updated: 2023-03-16Bibliographically approved
Berner, J., Moraes, A. L., Palm, B., Sanmartin Berglund, J. & Anderberg, P. (2023). Five-factor model, technology enthusiasm and technology anxiety. Digital Health, 9
Open this publication in new window or tab >>Five-factor model, technology enthusiasm and technology anxiety
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2023 (English)In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed) Published
Abstract [en]

Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online. © The Author(s) 2023.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
digital social participation, five-factor model, older adults, personality, Technology anxiety, technology enthusiasm
National Category
Gerontology, specialising in Medical and Health Sciences
Identifiers
urn:nbn:se:bth-25426 (URN)10.1177/20552076231203602 (DOI)001069602300001 ()2-s2.0-85171753514 (Scopus ID)
Available from: 2023-10-02 Created: 2023-10-02 Last updated: 2023-10-30Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Ali, A., Ali, L. & Anderberg, P. (2023). Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. Journal of medical systems, 47(1), Article ID 17.
Open this publication in new window or tab >>Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions
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2023 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 47, no 1, article id 17Article, review/survey (Refereed) Published
Abstract [en]

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 the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Deep learning, Dementia prediction, Feature selection, Machine learning, artificial intelligence, dementia, human, voice, Humans
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24274 (URN)10.1007/s10916-023-01906-7 (DOI)000920053700001 ()36720727 (PubMedID)2-s2.0-85147143895 (Scopus ID)
Note

Funding sponsor: NEAR - National E-Infrastructure for Aging Research

Available from: 2023-02-10 Created: 2023-02-10 Last updated: 2023-02-16Bibliographically approved
Flyborg, J., Renvert, S., Anderberg, P., Isaksson, U. & Sanmartin Berglund, J. (2023). Measurement of body temperature in the oral cavity with a temperature sensor integrated with a powered toothbrush. SN Applied Sciences, 5(1), Article ID 22.
Open this publication in new window or tab >>Measurement of body temperature in the oral cavity with a temperature sensor integrated with a powered toothbrush
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2023 (English)In: SN Applied Sciences, ISSN 2523-3963, E-ISSN 2523-3971, Vol. 5, no 1, article id 22Article in journal (Refereed) Published
Abstract [en]

This paper presents a method for collecting core body temperature data via a temperature sensor integrated into a powered toothbrush. The purpose is to facilitate the collection of temperature data without any extended effort from the user. Twelve participants use a powered toothbrush with a temperature sensor mounted on the brush head twice daily for two months. The obtained values are compared with those from a conventional fever thermometer approved for intraoral use. The results show that the temperature sensor–integrated powered toothbrush can measure the core body temperature and provide values comparable to those provided by a traditional oral thermometer. The use of the device can facilitate disease monitoring, fertility control, and security solutions for the elderly. © 2022, The Author(s).

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Core body temperature, Powered toothbrush, Thermometer, Data acquisition, Disease control, Physiology, Thermometers, Body temperature, Control solutions, Core body, Disease monitoring, Measurements of, Oral cavity, Security solutions, Temperature data, Temperature sensors
National Category
Dentistry
Identifiers
urn:nbn:se:bth-24175 (URN)10.1007/s42452-022-05250-2 (DOI)000898780700001 ()2-s2.0-85144436915 (Scopus ID)
Note

open access

Available from: 2023-01-12 Created: 2023-01-12 Last updated: 2024-02-08Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Ali, A., Anderberg, P. & Ali, L. (2023). Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks. Computers, Materials and Continua, 75(2), 2491-2508
Open this publication in new window or tab >>Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks
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2023 (English)In: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 75, no 2, p. 2491-2508Article in journal (Refereed) Published
Abstract [en]

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. © 2023 Tech Science Press. All rights reserved.

Place, publisher, year, edition, pages
Tech Science Press, 2023
Keywords
Dementia prediction, feature selection, genetic algorithm, neural networks, Deep neural networks, Diagnosis, Genetic algorithms, Learning systems, Neurodegenerative diseases, Cognitive decline, Detection/identification, Features selection, Neural-networks, Novel methods, Prediction modelling, Risk factors, Risk Identification, Societal impacts, Forecasting
National Category
Geriatrics Neurology
Identifiers
urn:nbn:se:bth-24482 (URN)10.32604/cmc.2023.033783 (DOI)000980836000009 ()2-s2.0-85152474169 (Scopus ID)
Projects
SNAC
Available from: 2023-05-01 Created: 2023-05-01 Last updated: 2023-05-26Bibliographically approved
Javeed, A., Sanmartin Berglund, J., Moraes, A. L., Saleem, M. A. & Anderberg, P. (2023). Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data. International Journal of Computational Intelligence Systems, 16(1), Article ID 188.
Open this publication in new window or tab >>Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
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2023 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 188Article in journal (Refereed) Published
Abstract [en]

Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2023
Keywords
Computer vision, Deep learning, Feature engineering, Sleep apnea, Blood pressure, Brain, Cardiology, Diagnosis, Diseases, E-learning, Health risks, Heart, Learning systems, Sleep research, Electronic health, Feature engineerings, Health data, Machine learning models, Memory network, Obstructive sleep apnea, Predictive power, Risk factors, Long short-term memory
National Category
Computer Sciences Neurosciences
Identifiers
urn:nbn:se:bth-25692 (URN)10.1007/s44196-023-00362-y (DOI)001114808900001 ()2-s2.0-85177889861 (Scopus ID)
Projects
NEAR - National E-Infrastructure for Aging Research
Available from: 2023-12-08 Created: 2023-12-08 Last updated: 2023-12-31Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9870-8477

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