Advancements in Healthcare: Predictive Modeling for Early Disease Diagnosis and Patient Record Management

This study introduces the Medical Unique Identification, a secure patient verification process designed for accessing medical records. Utilizing data mining techniques and machine learning algorithms, our focus is on early disease prediction based on user-provided symptoms. The developed classifier system aids physicians in early disease diagnosis, thus enhancing patient care. The machine learning model is trained on a dataset comprising 4920 patient records, each featuring 132 distinct symptoms. The study adopts the ID3 root-to-leaf approach for constructing the decision tree. Additionally, Naive Bayes theorem is applied to determine the probability of specific events. Patients can select symptoms from a list, and the disease with the highest confidence score is predicted and displayed. The system also integrates systematic storage of patient consultations, contributing to a more personalized and comprehensive patient history for improved healthcare and community services.