The COVID-19 pandemic has caused largescale morbidity and mortality and a tremendous burden on the healthcare system. Healthcare workers (HCWs) require adequate protection to avoid onward transmission and minimize burden on the healthcare system. Moreover, HCWs can also influence the general public into accepting the COVID-19 vaccine. Therefore, determining COVID-19 vaccine intention among HCWs is of paramount importance to plan tailor-made public health strategies to maximize vaccine coverage. A structured questionnaire was administered in February and March 2021 among HCWs in Saudi Arabia using convenience sampling, proceeding the launch of the vaccination campaign. HCWs from all administrative regions of Saudi Arabia were included in the study. In total, 674 out of 1124 HCWs responded and completed the survey (response rate 59.9%). About 65 percent of the HCWs intended to get vaccinated. The intention to vaccinate was significantly higher among HCWs 50 years of age or older, Saudi nationals and those who followed the updates about COVID-19 vaccines (p < 0.05). The high percentage (26 percent) of those who were undecided in getting vaccinated is a positive sign. As the vaccination campaign gathers pace, the attitude is expected to change over time. Emphasis should be on planning healthcare strategies to convince the undecided HCWs into accepting the vaccine in order to achieve the coverage required to achieve herd immunity.
Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.