The Medi AI: A Disease Prediction System is a machine learning-based application designed to assist in the early identification of diseases based on user-provided symptoms. In many cases, individuals ignore early symptoms due to lack of awareness, busy lifestyles, or limited access to healthcare facilities, which leads to delayed diagnosis and severe health complications. To address these challenges, this study proposes an intelligent system that predicts diseases using symptom-based input.

The system utilizes a dataset containing various diseases and their corresponding symptoms. Data preprocessing techniques such as missing value handling, encoding, normalization, and structuring are applied to improve data quality and enhance model performance. Multiple machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, and K-Nearest Neighbors are implemented and evaluated using performance metrics like accuracy, precision, recall, F1-score, and confusion matrix.

Experimental results indicate that ensemble models such as Random Forest provide higher prediction accuracy compared to other algorithms. The best-performing model is integrated into a web-based application that allows users to input symptoms and receive real-time disease predictions. The proposed system helps in early diagnosis, reduces dependency on immediate medical consultation, and improves healthcare accessibility, especially in remote areas.