Health Diagnostic System

The Health Diagnostic System is an AI-driven solution aimed at improving diagnostic accuracy and aiding healthcare professionals in the early detection of diseases. With 5% of outpatient diagnoses being incorrect each year, this project seeks to minimize diagnostic errors through machine learning-based predictions. We utilized various algorithms, such as K-Nearest Neighbors (KNN), Logistic Regression, and Random Forest, to discern patterns in patient symptoms and enhance diagnostic accuracy. The system evaluates input symptoms to predict potential diseases, offering healthcare providers a dependable decision-support tool.

Following extensive model evaluation, Random Forest proved to be the most accurate algorithm, achieving 92.15% accuracy after hyperparameter tuning. The model was trained on a diverse medical dataset, enabling it to generalize across various disease categories. A significant challenge during development was ensuring data privacy, security, and ethical compliance while managing sensitive medical data. Moreover, balancing high accuracy with interpretability was essential to ensuring its usability in real-world medical settings.

This project underscores the potential of machine learning in healthcare by delivering accurate and timely disease predictions. Future improvements may involve synthetic data generation to tackle privacy issues, transfer learning for better generalization across medical conditions, and integration with clinical systems for real-time diagnostics. With further advancements, the Health Diagnostic System could significantly impact patient care by reducing diagnostic errors and enhancing healthcare outcomes through data-driven insights.