Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-anonymity algorithm is utilized to prevent malicious users from accessing patients personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the Kanonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with kanonymity is 97.058\%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-anonymity has an accuracy score of 97.1429\%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions.