Abstract: The This chapter is focused on predicting cardiovascular diseases, and machine learning and neural network models are instrumental in this process, reducing human effort and providing accurate results. However, the challenge lies in interpreting the predictions made by these complex algorithms. To address this, the authors introduced Explainable Artificial Intelligence (XAI) to understand the reasoning behind the cardiovascular disease predictions. The authors used an explainable artificial neural network (ANN) with a multi-level model, achieving an impressive accuracy of 87%, outperforming other models.

On a different note, the ongoing SARS-CoV-2 (n- Coronavirus) pandemic has resulted in the loss of millions of lives worldwide. This virus can lead to severe respiratory illnesses such as pneumonia and severe acute respiratory syndrome (SARS), sometimes resulting in death. The asymptomatic nature of this sickness has made life and work more challenging for people. In this research, the authors focused on forecasting the global situation and impacts of the COVID-19 pandemic, utilizing the FbProphet model to predict new cases and deaths for the month of August. The goal of this study is to provide valuable insights to scientists, researchers, and the general public to aid in predicting and analyzing the effects of the epidemic. The study concludes that the virus's second wave was approximately four times stronger than the first. Additionally, the trajectory analysis of COVID-19 instances (monthly and weekly) revealed that the number of cases increased more during weekdays, possibly due to weekend lockdown measures. The application of the FbProphet model and other algorithms facilitated accurate predictions and improved the understanding of the COVID-19 situation.

Keywords: Artificial intelligence, Digital transformation, Healthcare, Implementation, Healthcare leaders, Organizational change, Qualitative methods.

PDF | DOI: 10.17148/IJARCCE.2023.12731

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