Personalized Predictive Healthcare Using Machine Learning and Generative AI

In the modern world, the convergence of technology and healthcare presents an opportunity to deliver individualized and reasonably priced health support at the user’s fingertips. This study offers a system that can serve as a personal health assistant by providing comprehensive health insights and early disease prediction. In order to predict possible health issues, users provide three primary symptoms and basic biographical information like age and gender. Using a voting ensemble model, the system generates a final prediction by integrating predictions from seven machine learning models: decision trees, random forests, naive bayes, logistic regression, SVM, K-nearest neighbors, and XGBoost. The system combines generative AI with predictive analytics to provide comprehensive explanations of the anticipated disease, including causes, symptoms, solutions, and available treatments. The generative AI model gives information that is tailored to each user’s needs by considering factors like age and gender. Evaluation measures that demonstrate the system’s efficacy and dependability include accuracy, precision, recall, and F1-score. By providing easily available health insights, this user-friendly platform raises awareness and makes it easier for people to make educated health decisions.