Correlated Naïve Bayes Algorithm to Determine Healing Rate of Hepatitis Patients

Abstract

The Correlated Naïve Bayes Algorithm is a statistical learning method that has shown promise in predicting the healing rate of hepatitis patients. Hepatitis is a liver disease that can be chronic or acute and affects millions of people worldwide. The healing rate of patients with hepatitis can vary widely depending on various factors such as age, gender, and medical history. The Correlated Naïve Bayes Algorithm takes into account the correlations between the different attributes of patients and their healing rates, unlike the traditional Naïve Bayes Algorithm. This approach has been shown to improve the accuracy of predictions significantly. In this study, the Correlated Naïve Bayes Algorithm was applied to a dataset of hepatitis patients. The dataset contained information about patients' age, gender, medical history, and other attributes that might affect their healing rates. The algorithm was trained on this dataset to predict the healing rate of new patients. The results showed that the Correlated Naïve Bayes Algorithm achieved higher accuracy in predicting the healing rate of hepatitis patients compared to the traditional Naïve Bayes Algorithm. This suggests that the Correlated Naïve Bayes Algorithm could be a useful tool for healthcare professionals in predicting the healing rate of hepatitis patients, and ultimately improving their treatment and care. Furthermore, the study also investigated the importance of different attributes in predicting the healing rate of hepatitis patients. The results showed that age and medical history were the most important factors, followed by gender and other attributes. The findings of this study have significant implications for the medical community, as accurate prediction of healing rates can inform treatment decisions and improve patient outcomes. The Correlated Naïve Bayes Algorithm provides a powerful tool for healthcare professionals in predicting the healing rate of hepatitis patients, and could be extended to other medical conditions. However, it is important to note that the Correlated Naïve Bayes Algorithm has limitations, such as the assumption of independence between attributes. Therefore, future research should investigate alternative methods that can overcome these limitations and improve the accuracy of predictions further.