The most affected cancer among women is breast cancer (BC). After skin cancer, the most affected cancer in the world is BC. The term re-occurrence is used for BC, which affects after successful treatment. The prediction of re-occurrence BC can reduce the chances of death. In this paper, we proposed a machine learning approach and neural network model to calculate recurrence chances of BC. We used the Wisconsin dataset of the UCI machine learning repository in our approach, implemented an artificial neural network (ANN), and compared the results with the support vector machine. Our model outperformed when compared to other models with an accuracy of 95% with support vector machine.KeywordsBreast cancerRe-occurrenceMachine learningPredictionArtificial neural network 

An Enhanced Approach to Predict Re-occurrences of Breast Cancer Using Machine Learning

The COVID-19, novel coronavirus, is a pandemic and contaminated disease and it is aggressively increasing day by day from the first occurrence. Till now, within three months, a total of more than twenty lacks people affected with viruses, and over 50 thousand people have died. The virus is mainly attacking the respiratory system of the human body. The cases were increasing exponentially from day by day. And the diagnostic test of COVID-19 is to detect the presence of viral proteins in a sample from a person’s respiratory tract. But this process of testing is taking more time, and it is cost effective. For the prediction of COVID-19, we proposed a deep learning-based convolution neural network model using a chest X-ray of humans. The CNN model is trained on the dataset of COVID-19 X-ray and normal human X-ray; so it will able to identify the density of pneumonia in the human body to detect the COVID-19 virus.

COVID-19 and SARS Virus Predictions from Chest X-ray Images Using a Deep Learning Model

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COVID-19 and SARS Virus Predictions from Chest X-ray Images Using a Deep Learning Model