CareGenic: AI/ML Based Interactive Medicines Prescription System

CareGenic leverages Artificial Intelligence (AI) and Machine Learning (ML) to develop a robust system for predicting illnesses based on symptoms and recommending appropriate medications. This study addresses the growing need for intelligent healthcare solutions that can assist medical professionals and improving patient outcomes. The system utilizes a Random Forest algorithm trained on extensive healthcare datasets to predict illnesses accurately. Furthermore, it recommends medications based on the predicted illnesses, ensuring a holistic approach to patient care. Implemented on a Raspberry Pi 3 with an interactive LCD display interface, CareGenic provides an accessible and efficient tool for healthcare environments. The system's performance was evaluated using metrics such as accuracy, precision, and recall, achieving 95%, 93%, and 92%, respectively. These results demonstrate the efficacy of CareGenic in real-world scenarios. Compared to existing systems like IBM Watson Health and Google Health, CareGenic offers a more integrated approach by combining symptom-based predictions with medication recommendations. Future work will focus on enhancing the model's accuracy and expanding its capabilities to cover a broader range of illnesses and medications. This paper provides detailed insights into the system's architecture, methodology, implementation, and evaluation, highlighting the significant potential of AI and ML in revolutionizing healthcare.