Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

Cancer is a complicated disease that produces deregulatory changes in cellular activities (such as proteins). Data from these levels must be integrated into multi-omics analyses to better understand cancer and its progression. Deep learning approaches have recently helped with multi-omics analysis of cancer data. Breast cancer is a prevalent form of cancer among women, resulting from a multitude of clinical, lifestyle, social, and economic factors. The goal of this study was to predict breast cancer using several machine learning methods. We applied the architecture for mono-omics data analysis of the Cancer Genome Atlas Breast Cancer datasets in our analytical investigation. The following classifiers were used: random forest, partial least squares, Naive Bayes, decision trees, neural networks, and Lasso regularization. They were used and evaluated using the area under the curve metric. The random forest classifier and the Lasso regularization classifier achieved the highest area under the curve values of 0.99 each. These areas under the curve values were obtained using the mono-omics data employed in this investigation. The random forest and Lasso regularization classifiers achieved the maximum prediction accuracy, showing that they are appropriate for this problem. For all mono-omics classification models used in this paper, random forest and Lasso regression offer the best results for all metrics (precision, recall, and F1 score). The integration of various risk factors in breast cancer prediction modeling can aid in early diagnosis and treatment, utilizing data collection, storage, and intelligent systems for disease management. The integration of diverse risk factors in breast cancer prediction modeling holds promise for early diagnosis and treatment. Leveraging data collection, storage, and intelligent systems can further enhance disease management strategies, ultimately contributing to improved patient outcomes.