ORCID Profile
0000-0001-9123-0618
Current Organisation
Khulna University of Engineering and Technology
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Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 05-2015
Publisher: Natural Sciences Publishing
Date: 05-2016
DOI: 10.18576/AMIS/100325
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 05-2014
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 12-2012
Publisher: IEEE
Date: 09-2016
Publisher: MDPI AG
Date: 28-09-2022
Abstract: Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Canadian Center of Science and Education
Date: 11-03-2014
DOI: 10.5539/MAS.V8N2P69
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 05-2016
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 30-06-2018
Publisher: MDPI AG
Date: 26-02-2021
DOI: 10.3390/S21051638
Abstract: The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and litudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients ( .98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz s ling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2015
Location: Bangladesh
No related grants have been discovered for Mohiuddin Ahmad.