AI-driven healthcare systems: A personalized symptom-based disease prognosis tool using RF, GNB, and SVC techniques.

Artificial intelligence (AI) technology is being leveraged for multiple tasks in the healthcare sector, such as improving the diagnosis of disease, streamlining management services, and tailoring treatment procedures. Applying predictive analysis and performing robotic surgery helps patients in a way that reduces the burden on their caregivers. This study uses a healthcare disease prediction dataset using three robust machine learning algorithms: random forest (RF), Gaussian Naïve Bayes (GNB), and support vector classifier (SVC). The models learn to use other symptoms to detect the presence of various diseases. In model evaluation, cross-validation is done on the training set after data preprocessing is performed to ensure none of the groups is overrepresented in the final model. In both the training and the testing of each model, which were respectively 100%, the model was able to make perfect predictions. A vote of three classifiers reached the 100% precision mark over a test dataset on an ensemble model that combined all the classifiers. This research integrates the advances in AI technology into the healthcare setting in a bid to enhance healthcare delivery. Additionally, we created such a straightforward tool in the case of input symptoms and proposed a possible disease diagnosis. This study also adds to the expanding corpus of research on AI in healthcare by providing a practical method for symptom-based diagnosis. [ABSTRACT FROM AUTHOR]