International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
Multiple disease detection using ML Roopa D E1, Anusha B T2, Bharath R Ghale3, Mohammed Maaz Ahmed4 1Assistant Professor, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India.
2,3,4 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Karnataka, India -----------------------------------------------------------------------***--------------------------------------------------------------------------
Abstract - The Multiple Disease Prediction System leverages
By allowing users to input relevant medical information such as age, blood pressure, cholesterol, and other markers, the system provides instant predictions and insights into disease risk. These results include visualizations that enhance understanding of critical features influencing each prediction, supporting early detection and empowering users to make informed health decisions. The project demonstrates both the computational feasibility and the clinical relevance of integrating machine learning models into real-world healthcare solutions. Additionally, its modular foundation supports the expansion to additional diseases or diagnostic metrics in future versions. The system’s open-source nature and flexible design encourage community contributions and adaptations, further extending its potential impact on global health informatics.
machine learning to provide an integrated, user-friendly web application for predicting several major diseases: diabetes, Parkinson’s disease, and heart disease. This platform utilizes disease-specific datasets and trained machine learning models to analyze user-provided medical information such as age, blood pressure, cholesterol, and other relevant parameters. The system’s interactive Streamlit interface enables individuals without technical expertise to easily input their data and receive predictions regarding their risk levels for each supported condition. Upon submitting the required details, users receive instant, clear indications of disease probabilities alongside visualizations that highlight crucial factors influencing each prediction. This approach combines accessible software design with robust data analytics, making it a practical aid in preliminary disease screening and awareness. The application demonstrates the potential of machine learning to empower healthcare systems through accurate, multi-disease prediction, contributing to early detection and improved patient outcomes. The modular structure allows for future expansion to additional diseases or health metrics, reflecting adaptability to broader medical domains. This research underscores both the computational feasibility and clinical relevance of integrating machine learning models within real-world digital health solutions.
1.1 Description The Multiple Disease Prediction System is a machine learning-based web application developed to predict the probability of three major health conditions: diabetes, Parkinson’s disease, and heart disease. The system uses individual disease-specific datasets and trained machine learning models to analyze user-provided medical information such as age, gender, blood pressure, cholesterol, and other relevant factors. By integrating these models into a single Streamlit-powered interface, the application enables users to input their health data and instantly receive predictions for each disease, along with visualizations that help explain the most important features influencing outcomes. Designed with accessibility and ease-of-use in mind, the platform is suitable for both healthcare professionals and laypersons, providing actionable insights through clear prediction results and graphical representations. The flexible architecture supports future expansion, allowing for the addition of new diseases or further enhancement of the user interface and prediction algorithms.
Key Words: Machine Learning, Disease Prediction, Diabetes, Heart Disease, Parkinson’s Disease, Streamlit, Medical Screening, Healthcare Analytics, Web Application.
1.INTRODUCTION The Multiple Disease Prediction System using Machine Learning is designed to address the growing need for accessible, efficient, and accurate health screening tools in modern healthcare environments. This project presents a web-based application that employs machine learning algorithms to predict the likelihood of diabetes, Parkinson's disease, and heart disease, based on disease-specific datasets and user-provided medical parameters. The implementation utilizes Streamlit, a Python framework that facilitates the creation of interactive web interfaces, enabling individuals without technical expertise to easily engage with advanced predictive models.
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Impact Factor value: 8.315
1.2 Existing System Sample The existing system for disease prediction commonly relies on individual applications or models to identify the risk of a single disease at a time, such as diabetes, Parkinson’s disease, or heart disease. These solutions usually require users to input relevant medical details and receive only one disease prediction per session, often lacking robust
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