Abstract

The early and accurate diagnosis of diseases is crucial for effective treatment and prevention. Traditional methods oftenrely on clinical tests and patient symptoms, which may not always be reliable or timely. We explore an innovative approach to diseasedetection using medical prescriptions. By analyzing historical prescription data, the study aims to identify patterns and correlationsbetween prescribed medications and specific diseases. Deep learning models, including classification and regression techniques, areapplied to a dataset of medical prescriptions to predict potential diagnoses. The model utilizes features and medical history to generateaccurate predictions. This approach not only enhances the speed and accuracy of disease detection but also provides a novel framework for personalized medicine, where treatments are aligned with a patient’s unique medical profile. The results demonstrate thatprescription based disease prediction can serve as a complementary tool in clinical decision-making, improving healthcare outcomesand streamlining diagnosis procedures.Keywords:- CNN, Preprocessing, Feature extraction, Deep learning prescription.