Disease Prediction System Using Artificial Intelligence and Machine Learning

Abstract

The rising incidence of chronic and infectious diseases has created an urgent need for smart tools that enable early diagnosis and effective monitoring. This solution offers a cloud-based system that uses machine learning and data visualization to help individuals, healthcare professionals, and public health authorities manage healthcare proactively and based on data. The system predicts the likelihood of diseases such as heart conditions, diabetes, and respiratory illnesses by analyzing user inputs like symptoms and medical history. It is trained on publicly available datasets from sources including Kaggle, the UCI Machine Learning Repository, and the World Health Organization (WHO). These datasets contain information such as age, gender, blood pressure, cholesterol levels, glucose levels, and lifestyle habits. The development process starts with data preprocessing, which involves cleaning, normalizing, handling missing values, and selecting important features. Supervised machine learning algorithms such as Logistic Regression, Random Forest, Decision Trees, and Naïve Bayes are trained and tested to find the most accurate model using standard evaluation metrics like accuracy, precision, and recall. Once the best model is chosen, it is deployed for real-time disease risk prediction via a user-friendly web interface built with Streamlit or Flask. A disease tracking module shows spread and trends of diseases over time using Plotly and Folium for interactive maps and charts. The system prioritizes user privacy and complies with GDPR and HIPAA standards. Future updates may include integration with wearable health devices, Electronic Health Records (EHRs), and Explainable AI techniques like SHAP and LIME.