NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms

Soui, Makram; Mansouri, Nesrine; Alhamad, Raed; Kessentini, Marouane; Ghédira, Khaled

doi:10.1007/s11071-021-06504-1

Cited by 42 publications

(20 citation statements)

References 53 publications

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“…For the most part, coronavirus spreads through person-to-person contact. The contamination starts with droplets from an infected person (cough, sneeze, or breath) ejected into the air or a surface that a healthy individual could breathe or touch, then touches his mouth, nose, or eyes [5] .…”

Section: Introductionmentioning

confidence: 99%

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Chamseddine

Mansouri²,

Soui³

et al. 2022

Applied Soft Computing

Self Cite

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Section: Introductionmentioning

confidence: 99%

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Chamseddine

Mansouri²,

Soui³

et al. 2022

Applied Soft Computing

Self Cite

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“…Among them, the common feature extraction methods are Scaleinvariant feature transform (SIFT) [7], Speeded Up Robust Features(SURF) [8], Histogram of Oriented Gradient (HOG) [9], etc. the common classi ers include support vector machines SVM) [10], Haar [11], Adaboost [12], etc. Remote sensing images have the characteristics of dense ground objects and complex environment, so the traditional target detection algorithm requires a lot of calculation and it is ine cient.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network Method for Remote Sensing Image Based Ground Objects Recognition

Xue

Technology

Zheng

et al. 2022

Preprint

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High resolution remote-sensing images have the characteristics of complex background environment, clustering of objects, etc., which lead to the problem of low accuracy in recognition of large ground objects such as airports, dams, golf field, etc. Based on this problem, this paper proposes a remote sensing image object detection method based on YOLOv5 network. By improving the backbone extraction network, the network structure can be deepened to get more information about large objects, the detection effect can be improved by adding attention mechanism and adding output layer to enhance feature extraction and feature fusion. The pre-training weight is obtained by transfer learning and used as the training weight of the improved YOLOv5 to speed up the network convergence. The experiment is carried out on DIOR dataset, the results show that the improved YOLOv5 network can significantly improve the accuracy of large object recognition compared with the YOLO series network and the EfficientDet model on DIOR datadet, and the mAP of the improved YOLOv5 network is 80.5%, which is 2% higher than the original YOLOv5 network.

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“…Therefore, automatically optimizing hyperparameters is a very tedious task. Various methods are already available in the literature, each with advantages and limitations 10 …”

Section: Introductionmentioning

confidence: 99%

“…Various methods are already available in the literature, each with advantages and limitations. 10 In this article, we design a predictive model for diabetes screening using routine test records of non-diabetic patients. A classification algorithm was trained on diabetes-positive patient data to calculate the impact and correlation between various metrics.…”

mentioning

confidence: 99%

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

Gupta

Rajput

Gunjan

et al. 2022

Concurrency and Computation

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Diabetes is one of the most prevalent causes of casualties in the modern world. Early diagnosis of diabetes is the most promising way for increasing the chances of patients' survival. The ever-growing technology of the current era, machine learning-based algorithms pave the door in the healthcare industry by delivering efficient decision support services in real-time. However, high-dimensionality of the data obtained using multiple sources increases the computation time and significantly impacts the models' efficiency in classifying the results. Feature selection improves learning performance and reduces the computational cost by selecting subsets of features and eliminating unnecessary and irrelevant features. In this article, an attempt has been made to develop a hybrid machine learning model based on non-dominated sorting genetic algorithm (NSGA-II) and ensemble learning for the efficient categorization of diabetes. The proposed work uses various data preprocessing techniques, such as missing data handling and normalization, prior to model training. The most prominent and salient features are selected by exploiting the potential of the NSGA-II in the diabetes dataset. Finally, an ensemble learning-based extreme gradient boosting (XGBoost) model is modeled using features selected by NSGA-II to classify patients as diabetic or non-diabetic. Theproposed methodology is experimentally validated using a hybridized dataset comprising 23 features, with 1288 instances of both male and female patients between the ages of 21 and 65. In addition, for performance evaluation, the results of statistical parameters are compared with several state-of-the-art decision-making models in the current domain. Experiment findings exemplify that the proposed NSGA-II-XGB approach gives better classification results with an average accuracy of 98.86%. Furthermore, the statistical results of specificity (88.6%), sensitivity (96.36%), and F-score (97.84%) also support the utility of the proposed methodology in the early diagnosis of diabetes.

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NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms

Cited by 42 publications

References 53 publications

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Improved YOLOv5 Network Method for Remote Sensing Image Based Ground Objects Recognition

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

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