Abstract:Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are β-Thalassemia carriers then there are 25% chances that each child will have β-Thalassemia intermediate or β-Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to cont… Show more
“…The topic of sarcasm detection is covered by Onan 31 by following a hybrid model approach while Sadiq et al 32 investigates aggression detection. The authors have explored many ML-based techniques using patients’ symptoms and laboratory reports during hospitalization 33 . Researchers are diligent in defeating COVID-19 by exploring ways of COVID-19 detection 34 and devising frameworks to control the spread of disease 35 .…”
Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including ‘not survived’, ‘recovered’, and ‘not recovered’ based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.
“…The topic of sarcasm detection is covered by Onan 31 by following a hybrid model approach while Sadiq et al 32 investigates aggression detection. The authors have explored many ML-based techniques using patients’ symptoms and laboratory reports during hospitalization 33 . Researchers are diligent in defeating COVID-19 by exploring ways of COVID-19 detection 34 and devising frameworks to control the spread of disease 35 .…”
Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including ‘not survived’, ‘recovered’, and ‘not recovered’ based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.
“…Sadiq et al 8 designed an aggregated classifier SGR-VC for the classification of -Thalassemia carriers and non-carriers. The proposed classifier is an ensemble of SVC, GBM, and RF which is trained and evaluated on the CBC data of 5066 patients from Punjab Thalassaemia Prevention Programme, Pakistan (PTPP).…”
Section: Related Workmentioning
confidence: 99%
“…A comparative analysis of discussed research works are provided in Table 1 which shows that lots of researchers have done work on -Thalassemia predictions but still accuracy and efficiency a gap to work in this domain. The imbalanced dataset is a problem in base work 8 that causes the model to over-fitting towards the majority class data and we work on this problem to achieve significant results. Figure 1 Dataset variable visualization.…”
Section: Related Workmentioning
confidence: 99%
“…CBC results contain several indicators that can be utilized to identify thalassemia carriers. Several approaches have been presented lately to detect thalassemia carriers 6 – 8 , however, such approaches are limited by the use of imbalanced datasets, lower classification accuracy, and less generalizability of models.…”
Abstract$$\beta $$
β
-Thalassemia is one of the dangerous causes of the high mortality rate in the Mediterranean countries. Substantial resources are required to save a $$\beta $$
β
-Thalassemia carriers’ life and early detection of thalassemia patients can help appropriate treatment to increase the carrier’s life expectancy. Being a genetic disease, it can not be prevented however the analysis of several indicators in parents’ blood can be used to detect disorders causing Thalassemia. Laboratory tests for Thalassemia are time-consuming and expensive like high-performance liquid chromatography, Complete Blood Count (CBC) with peripheral smear, genetic test, etc. Red blood indices from CBC can be used with machine learning models for the same task. Despite the available approaches for Thalassemia carriers from CBC data, gaps exist between the desired and achieved accuracy. Moreover, the data imbalance problem is studied well which makes the models less generalizable. This study proposes a highly accurate approach for $$\beta $$
β
-Thalassemia detection using red blood indices from CBC augmented by supervised machine learning. In view of the fact that all the features do not carry predictive information regarding the target variable, this study employs a unified framework of two features selection techniques including Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD). The data imbalance between $$\beta $$
β
-Thalassemia carrier and non-carriers is handled by Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN). Extensive experiments are performed using many state-of-the-art machine learning models and deep learning models. Experimental results indicate the superiority of the proposed approach over existing approaches with an accuracy score of 0.96.
“…Authors have explored many ML-based techniques using patients’ symptoms and laboratory reports during hospitalization [ 19 ]. Researchers are diligent in defeating COVID-19 by exploring ways of COVID-19 detection [ 20 ] and devising frameworks to control the spread of disease [ 21 ].…”
COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.
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