Unified Disease Prediction System Using Machine Learning Techniques

This innovative research uses datasets from Kaggle to leverage Machine Learning (ML) techniques to develop a complete unified disease prediction system. With its comprehensive coverage of diabetes, heart disease, and Parkinson's disease, the dataset offers a strong basis for predictive modelling. Support Vector Machines (SVM) are used in the study for Parkinson's and diabetes predictions, while logistic regression is used for heart disease predictions. The system's reliability and usability are improved by this varied computational approach, which guarantees nuanced forecasts for any medical condition. The popular area of interest is the nexus between new technology and healthcare, focusing on the critical role in data-driven decision-making, which plays an important role in preventative medicine. The research highlights the value of early diagnosis and preventative healthcare actions in addition to advancing healthcare technologies. By merging intuitive machine learning algorithms. For heart disease prediction using logistic regression model exhibited 81.96% accuracy, while SVM system for diabetes and Parkinson's disease exhibited 77.27 and 87.17%, respectively. The open-source Streamlit platform integration provides an intuitive user interface for the integrated module. This project's technology integration is an innovative way to build a more robust and adaptable healthcare system that promotes early detection and preventative treatment of prevalent health issues.