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- Javeed, Ashir, 1989-, et al.
(författare)
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Breaking barriers : a statistical and machine learning-based hybrid system for predicting dementia
- 2023
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Ingår i: Frontiers in Bioengineering and Biotechnology. - : Frontiers Media S.A.. - 2296-4185. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Predictive Power of XGBoost_BiLSTM Model : A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data
- 2023
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Ingår i: International Journal of Computational Intelligence Systems. - : Springer Nature. - 1875-6891 .- 1875-6883. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Wilroth, Johanna, et al.
(författare)
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Improving EEG-based decoding of the locus of auditory attention through domain adaptation
- 2023
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Ingår i: Journal of Neural Engineering. - : Institute of Physics (IOP). - 1741-2560 .- 1741-2552. ; 20:6
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Tidskriftsartikel (refereegranskat)abstract
- Objective. This paper presents a novel domain adaptation (DA) framework to enhance the accuracy of electroencephalography (EEG)-based auditory attention classification, specifically for classifying the direction (left or right) of attended speech. The framework aims to improve the performances for subjects with initially low classification accuracy, overcoming challenges posed by instrumental and human factors. Limited dataset size, variations in EEG data quality due to factors such as noise, electrode misplacement or subjects, and the need for generalization across different trials, conditions and subjects necessitate the use of DA methods. By leveraging DA methods, the framework can learn from one EEG dataset and adapt to another, potentially resulting in more reliable and robust classification models. Approach. This paper focuses on investigating a DA method, based on parallel transport, for addressing the auditory attention classification problem. The EEG data utilized in this study originates from an experiment where subjects were instructed to selectively attend to one of the two spatially separated voices presented simultaneously. Main results. Significant improvement in classification accuracy was observed when poor data from one subject was transported to the domain of good data from different subjects, as compared to the baseline. The mean classification accuracy for subjects with poor data increased from 45.84% to 67.92%. Specifically, the highest achieved classification accuracy from one subject reached 83.33%, a substantial increase from the baseline accuracy of 43.33%. Significance. The findings of our study demonstrate the improved classification performances achieved through the implementation of DA methods. This brings us a step closer to leveraging EEG in neuro-steered hearing devices. © 2023 The Author(s). Published by IOP Publishing Ltd.
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| 4. |
- Olsson, Pär A T, 1981-, et al.
(författare)
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Ab initio investigation of monoclinic phase stability and martensitic transformation in crystalline polyethylene
- 2018
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Ingår i: Physical Review Materials. - : American Physical Society. - 2475-9953. ; 2:7, s. 7-13
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Tidskriftsartikel (refereegranskat)abstract
- We study the phase stability and martensitic transformation of orthorhombic and monoclimic polyethylene by means of density functional theory using the nonempirical consistent-exchange vdW-DF-cx functional [Phys. Rev. B 89, 035412 (2014)]. The results show that the orthorhombic phase is the most stable of the two. Owing to the occurrence of soft librational phonon modes, the monoclimic phase is predicted not to be stable at zero pressure and temperature, but becomes stable when subjected to compressive transverse deformations that pin the chains and prevent them from wiggling freely. This theoretical characterization, or prediction, is consistent with the fact that the monoclimic phase is only observed experimentally when the material is subjected to mechanical loading. Also, the estimated threshold energy for the combination of lattice deformation associated with the T1 and T2 transformation paths (between the orthorhombic and monoclimic phases) and chain shuffling is found to be sufficiently low for thermally activated back transformations to occur. Thus, our prediction is that the crystalline part can transform back from the monoclimc to the orthorhombic phase upon unloading and/or annealing, which is consistent with experimental observations. Finally, we observe how a combination of such phase transformations can lead to a fold-plane reorientation from {110} to {100} type in a single orthorhombic crystal.
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