| 1. |
- Javeed, Ashir, 1989-, et al.
(författare)
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An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning
- 2022
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Ingår i: Life. - : MDPI. - 2075-1729. ; 12:7, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a neurological condition that primarily affects older adults and there is stillno cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 yearsbefore the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchershave presented several methods for early detection of dementia based on symptoms. However,these techniques suffer from two major flaws. The first issue is the bias of ML models caused byimbalanced classes in the dataset. Past research did not address this issue well and did not takepreventative precautions. Different ML models were developed to illustrate this bias. To alleviate theproblem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance thetraining process of the proposed ML model. The second issue is the poor classification accuracy ofML models, which leads to a limited clinical significance. To improve dementia prediction accuracy,we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boostmodel. The autoencoder is used to extract relevant features from the feature space and the Adaboostmodel is deployed for the classification of dementia by using an extracted subset of features. Thehyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimentalfindings reveal that the suggested learning system outperforms eleven similar systems which wereproposed in the literature. Furthermore, it was also observed that the proposed learning systemimproves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity.Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00%and specificity of 96.65%.
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| 2. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
- 2023
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Ingår i: Biomedicines. - : MDPI. - 2227-9059. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Kerren, Andreas, 1971-, et al.
(författare)
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CluMa-GO: Bring Gene Ontologies and Hierarchical Clusterings Together
- 2011
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Annan publikation (refereegranskat)abstract
- Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.
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| 4. |
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| 5. |
- Olsson, Anki, et al.
(författare)
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Better platelet function, less fibrinolysis and less hemolysis in re-transfused residual pump blood with the Ringer’s chase technique : a randomized pilot study
- 2018
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Ingår i: Perfusion. - : Sage Publications. - 0267-6591 .- 1477-111X. ; 33:3, s. 185-193
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Residual pump blood from the cardiopulmonary bypass (CPB) circuit is often collected into an infusion bag (IB) and re-transfused. An alternative is to chase the residual blood into the circulation through the arterial cannula with Ringer’s acetate. Our aim was to assess possible differences in hemostatic blood quality between these two techniques.Methods: Forty adult patients undergoing elective coronary artery bypass graft surgery with CPB were randomized to receive the residual pump blood by either an IB or through the Ringer’s chase (RC) technique. Platelet activation and function (impedance aggregometry), coagulation and hemolysis variables were assessed in the re-transfused blood and in the patients before, during and after surgery. Results are presented as median (25-75 quartiles).Results: Total hemoglobin and platelet levels in the re-transfused blood were comparable with the two methods, as were soluble platelet activation markers P-selectin and soluble glycoprotein VI (GPVI). Platelet aggregation (U) in the IB blood was significantly lower compared to the RC blood, with the agonists adenosine diphosphate (ADP) 24 (10-32) vs 46 (33-65), p<0.01, thrombin receptor activating peptide (TRAP) 50 (29-73) vs 69 (51-92), p=0.04 and collagen 24 (17-28) vs 34 (26-59), p<0.01. The IB blood had higher amounts of free hemoglobin (mg/L) (1086 (891-1717) vs 591(517-646), p<0.01) and D-dimer 0.60 (0.33-0.98) vs 0.3 (0.3-0.48), p<0.01. Other coagulation variables showed no difference between the groups. Conclusions: The handling of blood after CPB increases hemolysis, impairs platelet function and activates coagulation and fibrinolysis. The RC technique preserved the blood better than the commonly used IB technique.
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| 6. |
- Jusufi, Ilir, 1983-, et al.
(författare)
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Visualization of Mappings between the Gene Ontology and Cluster Trees
- 2012
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Ingår i: <em>Proceedings of the SPIE 2012 Conference on Visualization and Data Analysis (VDA '12)</em>. - : SPIE - International Society for Optical Engineering. - 9780819489418
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Konferensbidrag (refereegranskat)abstract
- Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set, but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This paper proposes a new visualization method for this task.
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