The Security Algorithm ES-BR22-001 Used to Secure the Health Data

The data age information is considerably more significant in open life, since individuals' well-being information just concluded regardless of whether COVID-19 impacted, and furthermore connected with all medical problems information. These information used to examine and anticipate the medical problems information by Machine Learning Algorithm, and afterward anticipated information need greater security. In this way, we applied the current strategy ChaCha technique and that strategy zeroed in as it were "encryption execution” so security is less. In this paper, to apply the new ES-BR22-001 strategy, this technique has 7 stages. The 1st stage is finding the K value. The 2nd stage is applying the K value in Eq. (1). The 3rd stage is finding the Sk values by using Eq. (1). The 4th stage is applying the Sk values in the sparse matrix. The 5th stage is sparse matrix values are converted into single line. The 6th stage is pairing all the values. The final stage is all paired values will be applied in the matrix. The new ES-BR22-001 method provides security and performance is good while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Security Algorithm BR22-01 Used to Secure the COVID'19 Health Data

The current generation data is most valuable in people's life, because data only decided people's health affected in COVID'19 or not, and not only COVID'19 all related to health issues data. To analyze and predict the health issue data by using Machine Learning Algorithm. This prediction issues data has most confidential data and need more security. So, applying the previous method is ChaCha method. This method focusing only performance not fully security. The new method is BR22-01. This method has five stages. The 1st stage is finding the secret key x & y value. The 2nd stage is applying key in Eq. (1). The 3rd stage is merge all values into single row then pair from left and swap the values in the HS matrix. The 4th stage is applying key in Eq. (2). The 5th stage is merge all values into single line then pair from left and swap the values in the HC matrix but reverse. The new method has provide good security as well as performance while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers.

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.