Ensemble-based Effective Diagnosis of Thyroid Disorder with Various Feature Selection Techniques

Akhtar, Tehseen; Arif, Saad; Mushtaq, Zohaib; Gilani, Syed Orner; Jamil, Mohsin; Ayaz, Yasar; Butt, Shahid Ikramullah

doi:10.1109/smarttech54121.2022.00019

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…This section will provide some benchmark results for future works in hyperspectral imagingbased crop classification in agricultural remote sensing. The accuracy of the classifier with the proposed methodology is measured in terms of the Average Accuracy (AA), Overall Accuracy (OA), and Cohen Kappa (CK) coefficient [27,28].…”

Section: Resultsmentioning

confidence: 99%

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Ali¹,

Mushtaq²,

Arif³

et al. 2023

Computer Systems Science and Engineering

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Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-

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Section: Resultsmentioning

confidence: 99%

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Ali¹,

Mushtaq²,

Arif³

et al. 2023

Computer Systems Science and Engineering

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“…Matthews correlation coefficient (MCC) represents the relationship between the actual and predicted responses in the presence of a class imbalance in the dataset ( Akhtar et al, 2022 ). It overcomes the bias effect in the predictions caused by the probability shift due to class imbalance and gives a reliable and balanced measure of the classifier’s prediction capability.…”

Section: Methodsmentioning

confidence: 99%

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

Arif

Munawar²,

Ali³

2023

Front. Physiol.

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Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks.Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics.Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen’s kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods.Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

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“…The experimental findings in [30] revealed that bagging ensemble integrated with three feature selection techniques including Recursive Feature Elimination (RFE), Select K-Best (SKB), and Select From Model (SFM) showed robust results in thyroid diagnosis. Similarly, Akhtar et al [31] selected attributes from the "thyroid 0387" dataset using RFE, SKB, and SFM. The authors developed an effective unified ensemble of ensembles for improved thyroid disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

SSC: The novel self-stack ensemble model for thyroid disease prediction

2024

PLoS ONE

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Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.

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Ensemble-based Effective Diagnosis of Thyroid Disorder with Various Feature Selection Techniques

Cited by 7 publications

References 21 publications

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology

SSC: The novel self-stack ensemble model for thyroid disease prediction

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