Leveraging SVM for Accurate Multiple Disease Prediction in Healthcare

Accurate disease prediction plays a vital role in healthcare, with recent advancements in machine learning (ML) and artificial intelligence (AI) driving this field forward. This paper presents a multi-disease prediction system that leverages advanced ML algorithms - Random Forest, Support Vector Machine (SVM), and Convolutional Neural Networks (CNNs) to predict diseases like diabetes, Parkinson's, and heart disease. By analyzing complex medical datasets, the system detects critical trends and risk factors. With an intuitive interface powered by Streamlit Cloud, users can select a specific disease, enter relevant health information, and receive customized predictions. This innovative tool improves early detection accuracy, providing proactive health management support for both individuals and healthcare providers. This system aids in precise and forecasts the patients profile and disease.