Disease Prediction and Doctor Recommender System

In the modern era of data-driven decision-making, early and accurate disease diagnosis has emerged as a critical challenge, particularly in resource-constrained settings. This paper proposes a machine learning-based disease prediction system using a Random Forest Classifier to forecast potential diseases based on symptoms provided by the user. The system is designed as a robust, interactive tool to aid in preliminary medical assessments. The model has been trained on a dataset comprising 4,920 records that span 133 symptoms and 41 unique diseases, using binary encoding to represent the presence or absence of each symptom. The core of the system is the Random Forest algorithm, chosen for its high accuracy, robustness, and ability to handle large feature spaces effectively. The classifier achieves an accuracy of approximately 97.6% on unseen test data, demonstrating strong predictive performance. The user can interact with the system via a Command Line Interface (CLI), inputting symptoms to receive a predicted disease along with a disclaimer highlighting the system’s advisory nature. In addition to disease prediction, the model provides a feature importance visualization, offering transparency into which symptoms most influence the outcome. This not only improves interpretability but also serves as a learning aid for users and researchers. This paper serves educational and research purposes, demonstrating how machine learning can supplement traditional diagnostics. It lays the groundwork for future enhancements, including web integration, confidence score reporting, and the inclusion of more comprehensive datasets. Ultimately, the system highlights the potential of AI-driven health tech to assist in preliminary diagnostics, especially where immediate medical consultation is unavailable.